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      "source": [
        "# Explore tests\n",
        "\n",
        "View and learn more about the tests available in the ValidMind Library, including code examples and usage of key functions.\n",
        "\n",
        "In this notebook, we'll dive deep into the utilities available for viewing and understanding the various tests that ValidMind provides through the `tests` module. Whether you're just getting started or looking for advanced tips, you'll find clear examples and explanations to assist you every step of the way.\n",
        "\n",
        "Before we go into the details, let's import the `describe_test` and `list_tests` functions from the `validmind.tests` module. These are the two functions that can be used to easily filter through tests and view details for individual tests.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from validmind.tests import (\n",
        "    describe_test,\n",
        "    list_tests,\n",
        "    list_tasks,\n",
        "    list_tags,\n",
        "    list_tasks_and_tags,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Contents\n",
        "\n",
        "- [Listing All Tests](#toc1_)\n",
        "- [Understanding Tags and Task Types](#toc2_)\n",
        "- [Searching for Specific Tests using `tags` and `tasks`](#toc3_)\n",
        "- [Delving into Test Details with `describe_test`](#toc4_)\n",
        "- [Next steps](#toc5_)\n",
        "  - [Discover more learning resources](#toc5_1_)\n",
        "\n",
        "<!-- vscode-jupyter-toc-config\n",
        "\tnumbering=false\n",
        "\tanchor=true\n",
        "\tflat=false\n",
        "\tminLevel=2\n",
        "\tmaxLevel=4\n",
        "\t/vscode-jupyter-toc-config -->\n",
        "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id='toc1_'></a>\n",
        "\n",
        "## [Listing All Tests](#toc0_)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `list_tests` function provides a convenient way to retrieve all available tests in the `validmind.tests` module. When invoked without any parameters, it returns a pandas DataFrame containing detailed information about each test.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
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              "}\n",
              "</style>\n",
              "<table id=\"T_87ae0\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_87ae0_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
              "      <th id=\"T_87ae0_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
              "      <th id=\"T_87ae0_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
              "      <th id=\"T_87ae0_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
              "      <th id=\"T_87ae0_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
              "      <th id=\"T_87ae0_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
              "      <th id=\"T_87ae0_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.ACFandPACFPlot</td>\n",
              "      <td id=\"T_87ae0_row0_col1\" class=\"data row0 col1\" >AC Fand PACF Plot</td>\n",
              "      <td id=\"T_87ae0_row0_col2\" class=\"data row0 col2\" >Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...</td>\n",
              "      <td id=\"T_87ae0_row0_col3\" class=\"data row0 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row0_col5\" class=\"data row0 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row0_col6\" class=\"data row0 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ADF</td>\n",
              "      <td id=\"T_87ae0_row1_col1\" class=\"data row1 col1\" >ADF</td>\n",
              "      <td id=\"T_87ae0_row1_col2\" class=\"data row1 col2\" >Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....</td>\n",
              "      <td id=\"T_87ae0_row1_col3\" class=\"data row1 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row1_col4\" class=\"data row1 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row1_col5\" class=\"data row1 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity']</td>\n",
              "      <td id=\"T_87ae0_row1_col6\" class=\"data row1 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.AutoAR</td>\n",
              "      <td id=\"T_87ae0_row2_col1\" class=\"data row2 col1\" >Auto AR</td>\n",
              "      <td id=\"T_87ae0_row2_col2\" class=\"data row2 col2\" >Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....</td>\n",
              "      <td id=\"T_87ae0_row2_col3\" class=\"data row2 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row2_col4\" class=\"data row2 col4\" >{'max_ar_order': {'type': 'int', 'default': 3}}</td>\n",
              "      <td id=\"T_87ae0_row2_col5\" class=\"data row2 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
              "      <td id=\"T_87ae0_row2_col6\" class=\"data row2 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.AutoMA</td>\n",
              "      <td id=\"T_87ae0_row3_col1\" class=\"data row3 col1\" >Auto MA</td>\n",
              "      <td id=\"T_87ae0_row3_col2\" class=\"data row3 col2\" >Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...</td>\n",
              "      <td id=\"T_87ae0_row3_col3\" class=\"data row3 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row3_col4\" class=\"data row3 col4\" >{'max_ma_order': {'type': 'int', 'default': 3}}</td>\n",
              "      <td id=\"T_87ae0_row3_col5\" class=\"data row3 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
              "      <td id=\"T_87ae0_row3_col6\" class=\"data row3 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.AutoStationarity</td>\n",
              "      <td id=\"T_87ae0_row4_col1\" class=\"data row4 col1\" >Auto Stationarity</td>\n",
              "      <td id=\"T_87ae0_row4_col2\" class=\"data row4 col2\" >Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....</td>\n",
              "      <td id=\"T_87ae0_row4_col3\" class=\"data row4 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row4_col4\" class=\"data row4 col4\" >{'max_order': {'type': 'int', 'default': 5}, 'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_87ae0_row4_col5\" class=\"data row4 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
              "      <td id=\"T_87ae0_row4_col6\" class=\"data row4 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
              "      <td id=\"T_87ae0_row5_col1\" class=\"data row5 col1\" >Bivariate Scatter Plots</td>\n",
              "      <td id=\"T_87ae0_row5_col2\" class=\"data row5 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
              "      <td id=\"T_87ae0_row5_col3\" class=\"data row5 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row5_col5\" class=\"data row5 col5\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row5_col6\" class=\"data row5 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.BoxPierce</td>\n",
              "      <td id=\"T_87ae0_row6_col1\" class=\"data row6 col1\" >Box Pierce</td>\n",
              "      <td id=\"T_87ae0_row6_col2\" class=\"data row6 col2\" >Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....</td>\n",
              "      <td id=\"T_87ae0_row6_col3\" class=\"data row6 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row6_col4\" class=\"data row6 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row6_col5\" class=\"data row6 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row6_col6\" class=\"data row6 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
              "      <td id=\"T_87ae0_row7_col1\" class=\"data row7 col1\" >Chi Squared Features Table</td>\n",
              "      <td id=\"T_87ae0_row7_col2\" class=\"data row7 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
              "      <td id=\"T_87ae0_row7_col3\" class=\"data row7 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row7_col4\" class=\"data row7 col4\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
              "      <td id=\"T_87ae0_row7_col5\" class=\"data row7 col5\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
              "      <td id=\"T_87ae0_row7_col6\" class=\"data row7 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.ClassImbalance</td>\n",
              "      <td id=\"T_87ae0_row8_col1\" class=\"data row8 col1\" >Class Imbalance</td>\n",
              "      <td id=\"T_87ae0_row8_col2\" class=\"data row8 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
              "      <td id=\"T_87ae0_row8_col3\" class=\"data row8 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row8_col4\" class=\"data row8 col4\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_87ae0_row8_col5\" class=\"data row8 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
              "      <td id=\"T_87ae0_row8_col6\" class=\"data row8 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.DatasetDescription</td>\n",
              "      <td id=\"T_87ae0_row9_col1\" class=\"data row9 col1\" >Dataset Description</td>\n",
              "      <td id=\"T_87ae0_row9_col2\" class=\"data row9 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
              "      <td id=\"T_87ae0_row9_col3\" class=\"data row9 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row9_col5\" class=\"data row9 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
              "      <td id=\"T_87ae0_row9_col6\" class=\"data row9 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.DatasetSplit</td>\n",
              "      <td id=\"T_87ae0_row10_col1\" class=\"data row10 col1\" >Dataset Split</td>\n",
              "      <td id=\"T_87ae0_row10_col2\" class=\"data row10 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
              "      <td id=\"T_87ae0_row10_col3\" class=\"data row10 col3\" >['datasets']</td>\n",
              "      <td id=\"T_87ae0_row10_col4\" class=\"data row10 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row10_col5\" class=\"data row10 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
              "      <td id=\"T_87ae0_row10_col6\" class=\"data row10 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
              "      <td id=\"T_87ae0_row11_col1\" class=\"data row11 col1\" >Descriptive Statistics</td>\n",
              "      <td id=\"T_87ae0_row11_col2\" class=\"data row11 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
              "      <td id=\"T_87ae0_row11_col3\" class=\"data row11 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row11_col4\" class=\"data row11 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row11_col5\" class=\"data row11 col5\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
              "      <td id=\"T_87ae0_row11_col6\" class=\"data row11 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.DickeyFullerGLS</td>\n",
              "      <td id=\"T_87ae0_row12_col1\" class=\"data row12 col1\" >Dickey Fuller GLS</td>\n",
              "      <td id=\"T_87ae0_row12_col2\" class=\"data row12 col2\" >Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration....</td>\n",
              "      <td id=\"T_87ae0_row12_col3\" class=\"data row12 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row12_col4\" class=\"data row12 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row12_col5\" class=\"data row12 col5\" >['time_series_data', 'forecasting', 'unit_root_test']</td>\n",
              "      <td id=\"T_87ae0_row12_col6\" class=\"data row12 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.Duplicates</td>\n",
              "      <td id=\"T_87ae0_row13_col1\" class=\"data row13 col1\" >Duplicates</td>\n",
              "      <td id=\"T_87ae0_row13_col2\" class=\"data row13 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
              "      <td id=\"T_87ae0_row13_col3\" class=\"data row13 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row13_col4\" class=\"data row13 col4\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
              "      <td id=\"T_87ae0_row13_col5\" class=\"data row13 col5\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
              "      <td id=\"T_87ae0_row13_col6\" class=\"data row13 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.EngleGrangerCoint</td>\n",
              "      <td id=\"T_87ae0_row14_col1\" class=\"data row14 col1\" >Engle Granger Coint</td>\n",
              "      <td id=\"T_87ae0_row14_col2\" class=\"data row14 col2\" >Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test....</td>\n",
              "      <td id=\"T_87ae0_row14_col3\" class=\"data row14 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row14_col4\" class=\"data row14 col4\" >{'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_87ae0_row14_col5\" class=\"data row14 col5\" >['time_series_data', 'statistical_test', 'forecasting']</td>\n",
              "      <td id=\"T_87ae0_row14_col6\" class=\"data row14 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
              "      <td id=\"T_87ae0_row15_col1\" class=\"data row15 col1\" >Feature Target Correlation Plot</td>\n",
              "      <td id=\"T_87ae0_row15_col2\" class=\"data row15 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
              "      <td id=\"T_87ae0_row15_col3\" class=\"data row15 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row15_col4\" class=\"data row15 col4\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
              "      <td id=\"T_87ae0_row15_col5\" class=\"data row15 col5\" >['tabular_data', 'visualization', 'correlation']</td>\n",
              "      <td id=\"T_87ae0_row15_col6\" class=\"data row15 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.HighCardinality</td>\n",
              "      <td id=\"T_87ae0_row16_col1\" class=\"data row16 col1\" >High Cardinality</td>\n",
              "      <td id=\"T_87ae0_row16_col2\" class=\"data row16 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
              "      <td id=\"T_87ae0_row16_col3\" class=\"data row16 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row16_col4\" class=\"data row16 col4\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
              "      <td id=\"T_87ae0_row16_col5\" class=\"data row16 col5\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
              "      <td id=\"T_87ae0_row16_col6\" class=\"data row16 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
              "      <td id=\"T_87ae0_row17_col1\" class=\"data row17 col1\" >High Pearson Correlation</td>\n",
              "      <td id=\"T_87ae0_row17_col2\" class=\"data row17 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
              "      <td id=\"T_87ae0_row17_col3\" class=\"data row17 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row17_col4\" class=\"data row17 col4\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row17_col5\" class=\"data row17 col5\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
              "      <td id=\"T_87ae0_row17_col6\" class=\"data row17 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
              "      <td id=\"T_87ae0_row18_col1\" class=\"data row18 col1\" >IQR Outliers Bar Plot</td>\n",
              "      <td id=\"T_87ae0_row18_col2\" class=\"data row18 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
              "      <td id=\"T_87ae0_row18_col3\" class=\"data row18 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row18_col4\" class=\"data row18 col4\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
              "      <td id=\"T_87ae0_row18_col5\" class=\"data row18 col5\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
              "      <td id=\"T_87ae0_row18_col6\" class=\"data row18 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
              "      <td id=\"T_87ae0_row19_col1\" class=\"data row19 col1\" >IQR Outliers Table</td>\n",
              "      <td id=\"T_87ae0_row19_col2\" class=\"data row19 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
              "      <td id=\"T_87ae0_row19_col3\" class=\"data row19 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row19_col4\" class=\"data row19 col4\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
              "      <td id=\"T_87ae0_row19_col5\" class=\"data row19 col5\" >['tabular_data', 'numerical_data']</td>\n",
              "      <td id=\"T_87ae0_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
              "      <td id=\"T_87ae0_row20_col1\" class=\"data row20 col1\" >Isolation Forest Outliers</td>\n",
              "      <td id=\"T_87ae0_row20_col2\" class=\"data row20 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
              "      <td id=\"T_87ae0_row20_col3\" class=\"data row20 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row20_col4\" class=\"data row20 col4\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row20_col5\" class=\"data row20 col5\" >['tabular_data', 'anomaly_detection']</td>\n",
              "      <td id=\"T_87ae0_row20_col6\" class=\"data row20 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.JarqueBera</td>\n",
              "      <td id=\"T_87ae0_row21_col1\" class=\"data row21 col1\" >Jarque Bera</td>\n",
              "      <td id=\"T_87ae0_row21_col2\" class=\"data row21 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
              "      <td id=\"T_87ae0_row21_col3\" class=\"data row21 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row21_col4\" class=\"data row21 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row21_col5\" class=\"data row21 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row21_col6\" class=\"data row21 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.KPSS</td>\n",
              "      <td id=\"T_87ae0_row22_col1\" class=\"data row22 col1\" >KPSS</td>\n",
              "      <td id=\"T_87ae0_row22_col2\" class=\"data row22 col2\" >Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test....</td>\n",
              "      <td id=\"T_87ae0_row22_col3\" class=\"data row22 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row22_col4\" class=\"data row22 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row22_col5\" class=\"data row22 col5\" >['time_series_data', 'stationarity', 'unit_root_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row22_col6\" class=\"data row22 col6\" >['data_validation']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.LJungBox</td>\n",
              "      <td id=\"T_87ae0_row23_col1\" class=\"data row23 col1\" >L Jung Box</td>\n",
              "      <td id=\"T_87ae0_row23_col2\" class=\"data row23 col2\" >Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....</td>\n",
              "      <td id=\"T_87ae0_row23_col3\" class=\"data row23 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row23_col4\" class=\"data row23 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row23_col5\" class=\"data row23 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row23_col6\" class=\"data row23 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.LaggedCorrelationHeatmap</td>\n",
              "      <td id=\"T_87ae0_row24_col1\" class=\"data row24 col1\" >Lagged Correlation Heatmap</td>\n",
              "      <td id=\"T_87ae0_row24_col2\" class=\"data row24 col2\" >Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...</td>\n",
              "      <td id=\"T_87ae0_row24_col3\" class=\"data row24 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row24_col4\" class=\"data row24 col4\" >{'num_lags': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_87ae0_row24_col5\" class=\"data row24 col5\" >['time_series_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row24_col6\" class=\"data row24 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.MissingValues</td>\n",
              "      <td id=\"T_87ae0_row25_col1\" class=\"data row25 col1\" >Missing Values</td>\n",
              "      <td id=\"T_87ae0_row25_col2\" class=\"data row25 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
              "      <td id=\"T_87ae0_row25_col3\" class=\"data row25 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row25_col4\" class=\"data row25 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
              "      <td id=\"T_87ae0_row25_col5\" class=\"data row25 col5\" >['tabular_data', 'data_quality']</td>\n",
              "      <td id=\"T_87ae0_row25_col6\" class=\"data row25 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
              "      <td id=\"T_87ae0_row26_col1\" class=\"data row26 col1\" >Missing Values Bar Plot</td>\n",
              "      <td id=\"T_87ae0_row26_col2\" class=\"data row26 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
              "      <td id=\"T_87ae0_row26_col3\" class=\"data row26 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row26_col4\" class=\"data row26 col4\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
              "      <td id=\"T_87ae0_row26_col5\" class=\"data row26 col5\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row26_col6\" class=\"data row26 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.MutualInformation</td>\n",
              "      <td id=\"T_87ae0_row27_col1\" class=\"data row27 col1\" >Mutual Information</td>\n",
              "      <td id=\"T_87ae0_row27_col2\" class=\"data row27 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
              "      <td id=\"T_87ae0_row27_col3\" class=\"data row27 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row27_col4\" class=\"data row27 col4\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
              "      <td id=\"T_87ae0_row27_col5\" class=\"data row27 col5\" >['feature_selection', 'data_analysis']</td>\n",
              "      <td id=\"T_87ae0_row27_col6\" class=\"data row27 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
              "      <td id=\"T_87ae0_row28_col1\" class=\"data row28 col1\" >Pearson Correlation Matrix</td>\n",
              "      <td id=\"T_87ae0_row28_col2\" class=\"data row28 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
              "      <td id=\"T_87ae0_row28_col3\" class=\"data row28 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row28_col5\" class=\"data row28 col5\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
              "      <td id=\"T_87ae0_row28_col6\" class=\"data row28 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.PhillipsPerronArch</td>\n",
              "      <td id=\"T_87ae0_row29_col1\" class=\"data row29 col1\" >Phillips Perron Arch</td>\n",
              "      <td id=\"T_87ae0_row29_col2\" class=\"data row29 col2\" >Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test....</td>\n",
              "      <td id=\"T_87ae0_row29_col3\" class=\"data row29 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row29_col4\" class=\"data row29 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row29_col5\" class=\"data row29 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'unit_root_test']</td>\n",
              "      <td id=\"T_87ae0_row29_col6\" class=\"data row29 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
              "      <td id=\"T_87ae0_row30_col1\" class=\"data row30 col1\" >Protected Classes Description</td>\n",
              "      <td id=\"T_87ae0_row30_col2\" class=\"data row30 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
              "      <td id=\"T_87ae0_row30_col3\" class=\"data row30 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row30_col4\" class=\"data row30 col4\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row30_col5\" class=\"data row30 col5\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
              "      <td id=\"T_87ae0_row30_col6\" class=\"data row30 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.RollingStatsPlot</td>\n",
              "      <td id=\"T_87ae0_row31_col1\" class=\"data row31 col1\" >Rolling Stats Plot</td>\n",
              "      <td id=\"T_87ae0_row31_col2\" class=\"data row31 col2\" >Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified...</td>\n",
              "      <td id=\"T_87ae0_row31_col3\" class=\"data row31 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row31_col4\" class=\"data row31 col4\" >{'window_size': {'type': 'int', 'default': 12}}</td>\n",
              "      <td id=\"T_87ae0_row31_col5\" class=\"data row31 col5\" >['time_series_data', 'visualization', 'stationarity']</td>\n",
              "      <td id=\"T_87ae0_row31_col6\" class=\"data row31 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.RunsTest</td>\n",
              "      <td id=\"T_87ae0_row32_col1\" class=\"data row32 col1\" >Runs Test</td>\n",
              "      <td id=\"T_87ae0_row32_col2\" class=\"data row32 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
              "      <td id=\"T_87ae0_row32_col3\" class=\"data row32 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row32_col4\" class=\"data row32 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row32_col5\" class=\"data row32 col5\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row32_col6\" class=\"data row32 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row33_col0\" class=\"data row33 col0\" >validmind.data_validation.ScatterPlot</td>\n",
              "      <td id=\"T_87ae0_row33_col1\" class=\"data row33 col1\" >Scatter Plot</td>\n",
              "      <td id=\"T_87ae0_row33_col2\" class=\"data row33 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
              "      <td id=\"T_87ae0_row33_col3\" class=\"data row33 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row33_col4\" class=\"data row33 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row33_col5\" class=\"data row33 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row33_col6\" class=\"data row33 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row34_col0\" class=\"data row34 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
              "      <td id=\"T_87ae0_row34_col1\" class=\"data row34 col1\" >Score Band Default Rates</td>\n",
              "      <td id=\"T_87ae0_row34_col2\" class=\"data row34 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
              "      <td id=\"T_87ae0_row34_col3\" class=\"data row34 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row34_col4\" class=\"data row34 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row34_col5\" class=\"data row34 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
              "      <td id=\"T_87ae0_row34_col6\" class=\"data row34 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row35_col0\" class=\"data row35 col0\" >validmind.data_validation.SeasonalDecompose</td>\n",
              "      <td id=\"T_87ae0_row35_col1\" class=\"data row35 col1\" >Seasonal Decompose</td>\n",
              "      <td id=\"T_87ae0_row35_col2\" class=\"data row35 col2\" >Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components....</td>\n",
              "      <td id=\"T_87ae0_row35_col3\" class=\"data row35 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row35_col4\" class=\"data row35 col4\" >{'seasonal_model': {'type': 'str', 'default': 'additive'}}</td>\n",
              "      <td id=\"T_87ae0_row35_col5\" class=\"data row35 col5\" >['time_series_data', 'seasonality', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row35_col6\" class=\"data row35 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row36_col0\" class=\"data row36 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
              "      <td id=\"T_87ae0_row36_col1\" class=\"data row36 col1\" >Shapiro Wilk</td>\n",
              "      <td id=\"T_87ae0_row36_col2\" class=\"data row36 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
              "      <td id=\"T_87ae0_row36_col3\" class=\"data row36 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row36_col4\" class=\"data row36 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row36_col5\" class=\"data row36 col5\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
              "      <td id=\"T_87ae0_row36_col6\" class=\"data row36 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row37_col0\" class=\"data row37 col0\" >validmind.data_validation.Skewness</td>\n",
              "      <td id=\"T_87ae0_row37_col1\" class=\"data row37 col1\" >Skewness</td>\n",
              "      <td id=\"T_87ae0_row37_col2\" class=\"data row37 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
              "      <td id=\"T_87ae0_row37_col3\" class=\"data row37 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row37_col4\" class=\"data row37 col4\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
              "      <td id=\"T_87ae0_row37_col5\" class=\"data row37 col5\" >['data_quality', 'tabular_data']</td>\n",
              "      <td id=\"T_87ae0_row37_col6\" class=\"data row37 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row38_col0\" class=\"data row38 col0\" >validmind.data_validation.SpreadPlot</td>\n",
              "      <td id=\"T_87ae0_row38_col1\" class=\"data row38 col1\" >Spread Plot</td>\n",
              "      <td id=\"T_87ae0_row38_col2\" class=\"data row38 col2\" >Assesses potential correlations between pairs of time series variables through visualization to enhance...</td>\n",
              "      <td id=\"T_87ae0_row38_col3\" class=\"data row38 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row38_col4\" class=\"data row38 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row38_col5\" class=\"data row38 col5\" >['time_series_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row38_col6\" class=\"data row38 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row39_col0\" class=\"data row39 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
              "      <td id=\"T_87ae0_row39_col1\" class=\"data row39 col1\" >Tabular Categorical Bar Plots</td>\n",
              "      <td id=\"T_87ae0_row39_col2\" class=\"data row39 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
              "      <td id=\"T_87ae0_row39_col3\" class=\"data row39 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row39_col4\" class=\"data row39 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row39_col5\" class=\"data row39 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row39_col6\" class=\"data row39 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row40_col0\" class=\"data row40 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
              "      <td id=\"T_87ae0_row40_col1\" class=\"data row40 col1\" >Tabular Date Time Histograms</td>\n",
              "      <td id=\"T_87ae0_row40_col2\" class=\"data row40 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
              "      <td id=\"T_87ae0_row40_col3\" class=\"data row40 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row40_col4\" class=\"data row40 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row40_col5\" class=\"data row40 col5\" >['time_series_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row40_col6\" class=\"data row40 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row41_col0\" class=\"data row41 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
              "      <td id=\"T_87ae0_row41_col1\" class=\"data row41 col1\" >Tabular Description Tables</td>\n",
              "      <td id=\"T_87ae0_row41_col2\" class=\"data row41 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
              "      <td id=\"T_87ae0_row41_col3\" class=\"data row41 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row41_col4\" class=\"data row41 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row41_col5\" class=\"data row41 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_87ae0_row41_col6\" class=\"data row41 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row42_col0\" class=\"data row42 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
              "      <td id=\"T_87ae0_row42_col1\" class=\"data row42 col1\" >Tabular Numerical Histograms</td>\n",
              "      <td id=\"T_87ae0_row42_col2\" class=\"data row42 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
              "      <td id=\"T_87ae0_row42_col3\" class=\"data row42 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row42_col4\" class=\"data row42 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row42_col5\" class=\"data row42 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row42_col6\" class=\"data row42 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row43_col0\" class=\"data row43 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
              "      <td id=\"T_87ae0_row43_col1\" class=\"data row43 col1\" >Target Rate Bar Plots</td>\n",
              "      <td id=\"T_87ae0_row43_col2\" class=\"data row43 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
              "      <td id=\"T_87ae0_row43_col3\" class=\"data row43 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row43_col4\" class=\"data row43 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row43_col5\" class=\"data row43 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
              "      <td id=\"T_87ae0_row43_col6\" class=\"data row43 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row44_col0\" class=\"data row44 col0\" >validmind.data_validation.TimeSeriesDescription</td>\n",
              "      <td id=\"T_87ae0_row44_col1\" class=\"data row44 col1\" >Time Series Description</td>\n",
              "      <td id=\"T_87ae0_row44_col2\" class=\"data row44 col2\" >Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,...</td>\n",
              "      <td id=\"T_87ae0_row44_col3\" class=\"data row44 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row44_col4\" class=\"data row44 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row44_col5\" class=\"data row44 col5\" >['time_series_data', 'analysis']</td>\n",
              "      <td id=\"T_87ae0_row44_col6\" class=\"data row44 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row45_col0\" class=\"data row45 col0\" >validmind.data_validation.TimeSeriesDescriptiveStatistics</td>\n",
              "      <td id=\"T_87ae0_row45_col1\" class=\"data row45 col1\" >Time Series Descriptive Statistics</td>\n",
              "      <td id=\"T_87ae0_row45_col2\" class=\"data row45 col2\" >Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues....</td>\n",
              "      <td id=\"T_87ae0_row45_col3\" class=\"data row45 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row45_col4\" class=\"data row45 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row45_col5\" class=\"data row45 col5\" >['time_series_data', 'analysis']</td>\n",
              "      <td id=\"T_87ae0_row45_col6\" class=\"data row45 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row46_col0\" class=\"data row46 col0\" >validmind.data_validation.TimeSeriesFrequency</td>\n",
              "      <td id=\"T_87ae0_row46_col1\" class=\"data row46 col1\" >Time Series Frequency</td>\n",
              "      <td id=\"T_87ae0_row46_col2\" class=\"data row46 col2\" >Evaluates consistency of time series data frequency and generates a frequency plot....</td>\n",
              "      <td id=\"T_87ae0_row46_col3\" class=\"data row46 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row46_col4\" class=\"data row46 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row46_col5\" class=\"data row46 col5\" >['time_series_data']</td>\n",
              "      <td id=\"T_87ae0_row46_col6\" class=\"data row46 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row47_col0\" class=\"data row47 col0\" >validmind.data_validation.TimeSeriesHistogram</td>\n",
              "      <td id=\"T_87ae0_row47_col1\" class=\"data row47 col1\" >Time Series Histogram</td>\n",
              "      <td id=\"T_87ae0_row47_col2\" class=\"data row47 col2\" >Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....</td>\n",
              "      <td id=\"T_87ae0_row47_col3\" class=\"data row47 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row47_col4\" class=\"data row47 col4\" >{'nbins': {'type': '_empty', 'default': 30}}</td>\n",
              "      <td id=\"T_87ae0_row47_col5\" class=\"data row47 col5\" >['data_validation', 'visualization', 'time_series_data']</td>\n",
              "      <td id=\"T_87ae0_row47_col6\" class=\"data row47 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row48_col0\" class=\"data row48 col0\" >validmind.data_validation.TimeSeriesLinePlot</td>\n",
              "      <td id=\"T_87ae0_row48_col1\" class=\"data row48 col1\" >Time Series Line Plot</td>\n",
              "      <td id=\"T_87ae0_row48_col2\" class=\"data row48 col2\" >Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....</td>\n",
              "      <td id=\"T_87ae0_row48_col3\" class=\"data row48 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row48_col4\" class=\"data row48 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row48_col5\" class=\"data row48 col5\" >['time_series_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row48_col6\" class=\"data row48 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row49_col0\" class=\"data row49 col0\" >validmind.data_validation.TimeSeriesMissingValues</td>\n",
              "      <td id=\"T_87ae0_row49_col1\" class=\"data row49 col1\" >Time Series Missing Values</td>\n",
              "      <td id=\"T_87ae0_row49_col2\" class=\"data row49 col2\" >Validates time-series data quality by confirming the count of missing values is below a certain threshold....</td>\n",
              "      <td id=\"T_87ae0_row49_col3\" class=\"data row49 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row49_col4\" class=\"data row49 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
              "      <td id=\"T_87ae0_row49_col5\" class=\"data row49 col5\" >['time_series_data']</td>\n",
              "      <td id=\"T_87ae0_row49_col6\" class=\"data row49 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row50_col0\" class=\"data row50 col0\" >validmind.data_validation.TimeSeriesOutliers</td>\n",
              "      <td id=\"T_87ae0_row50_col1\" class=\"data row50 col1\" >Time Series Outliers</td>\n",
              "      <td id=\"T_87ae0_row50_col2\" class=\"data row50 col2\" >Identifies and visualizes outliers in time-series data using the z-score method....</td>\n",
              "      <td id=\"T_87ae0_row50_col3\" class=\"data row50 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row50_col4\" class=\"data row50 col4\" >{'zscore_threshold': {'type': 'int', 'default': 3}}</td>\n",
              "      <td id=\"T_87ae0_row50_col5\" class=\"data row50 col5\" >['time_series_data']</td>\n",
              "      <td id=\"T_87ae0_row50_col6\" class=\"data row50 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row51_col0\" class=\"data row51 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
              "      <td id=\"T_87ae0_row51_col1\" class=\"data row51 col1\" >Too Many Zero Values</td>\n",
              "      <td id=\"T_87ae0_row51_col2\" class=\"data row51 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
              "      <td id=\"T_87ae0_row51_col3\" class=\"data row51 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row51_col4\" class=\"data row51 col4\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
              "      <td id=\"T_87ae0_row51_col5\" class=\"data row51 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_87ae0_row51_col6\" class=\"data row51 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row52_col0\" class=\"data row52 col0\" >validmind.data_validation.UniqueRows</td>\n",
              "      <td id=\"T_87ae0_row52_col1\" class=\"data row52 col1\" >Unique Rows</td>\n",
              "      <td id=\"T_87ae0_row52_col2\" class=\"data row52 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
              "      <td id=\"T_87ae0_row52_col3\" class=\"data row52 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row52_col4\" class=\"data row52 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
              "      <td id=\"T_87ae0_row52_col5\" class=\"data row52 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_87ae0_row52_col6\" class=\"data row52 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row53_col0\" class=\"data row53 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
              "      <td id=\"T_87ae0_row53_col1\" class=\"data row53 col1\" >WOE Bin Plots</td>\n",
              "      <td id=\"T_87ae0_row53_col2\" class=\"data row53 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
              "      <td id=\"T_87ae0_row53_col3\" class=\"data row53 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row53_col4\" class=\"data row53 col4\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
              "      <td id=\"T_87ae0_row53_col5\" class=\"data row53 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
              "      <td id=\"T_87ae0_row53_col6\" class=\"data row53 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row54_col0\" class=\"data row54 col0\" >validmind.data_validation.WOEBinTable</td>\n",
              "      <td id=\"T_87ae0_row54_col1\" class=\"data row54 col1\" >WOE Bin Table</td>\n",
              "      <td id=\"T_87ae0_row54_col2\" class=\"data row54 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
              "      <td id=\"T_87ae0_row54_col3\" class=\"data row54 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row54_col4\" class=\"data row54 col4\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row54_col5\" class=\"data row54 col5\" >['tabular_data', 'categorical_data']</td>\n",
              "      <td id=\"T_87ae0_row54_col6\" class=\"data row54 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row55_col0\" class=\"data row55 col0\" >validmind.data_validation.ZivotAndrewsArch</td>\n",
              "      <td id=\"T_87ae0_row55_col1\" class=\"data row55 col1\" >Zivot Andrews Arch</td>\n",
              "      <td id=\"T_87ae0_row55_col2\" class=\"data row55 col2\" >Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test....</td>\n",
              "      <td id=\"T_87ae0_row55_col3\" class=\"data row55 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row55_col4\" class=\"data row55 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row55_col5\" class=\"data row55 col5\" >['time_series_data', 'stationarity', 'unit_root_test']</td>\n",
              "      <td id=\"T_87ae0_row55_col6\" class=\"data row55 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row56_col0\" class=\"data row56 col0\" >validmind.data_validation.nlp.CommonWords</td>\n",
              "      <td id=\"T_87ae0_row56_col1\" class=\"data row56 col1\" >Common Words</td>\n",
              "      <td id=\"T_87ae0_row56_col2\" class=\"data row56 col2\" >Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns....</td>\n",
              "      <td id=\"T_87ae0_row56_col3\" class=\"data row56 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row56_col4\" class=\"data row56 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row56_col5\" class=\"data row56 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
              "      <td id=\"T_87ae0_row56_col6\" class=\"data row56 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row57_col0\" class=\"data row57 col0\" >validmind.data_validation.nlp.Hashtags</td>\n",
              "      <td id=\"T_87ae0_row57_col1\" class=\"data row57 col1\" >Hashtags</td>\n",
              "      <td id=\"T_87ae0_row57_col2\" class=\"data row57 col2\" >Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....</td>\n",
              "      <td id=\"T_87ae0_row57_col3\" class=\"data row57 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row57_col4\" class=\"data row57 col4\" >{'top_hashtags': {'type': 'int', 'default': 25}}</td>\n",
              "      <td id=\"T_87ae0_row57_col5\" class=\"data row57 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
              "      <td id=\"T_87ae0_row57_col6\" class=\"data row57 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row58_col0\" class=\"data row58 col0\" >validmind.data_validation.nlp.LanguageDetection</td>\n",
              "      <td id=\"T_87ae0_row58_col1\" class=\"data row58 col1\" >Language Detection</td>\n",
              "      <td id=\"T_87ae0_row58_col2\" class=\"data row58 col2\" >Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages....</td>\n",
              "      <td id=\"T_87ae0_row58_col3\" class=\"data row58 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row58_col4\" class=\"data row58 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row58_col5\" class=\"data row58 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row58_col6\" class=\"data row58 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row59_col0\" class=\"data row59 col0\" >validmind.data_validation.nlp.Mentions</td>\n",
              "      <td id=\"T_87ae0_row59_col1\" class=\"data row59 col1\" >Mentions</td>\n",
              "      <td id=\"T_87ae0_row59_col2\" class=\"data row59 col2\" >Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....</td>\n",
              "      <td id=\"T_87ae0_row59_col3\" class=\"data row59 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row59_col4\" class=\"data row59 col4\" >{'top_mentions': {'type': 'int', 'default': 25}}</td>\n",
              "      <td id=\"T_87ae0_row59_col5\" class=\"data row59 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
              "      <td id=\"T_87ae0_row59_col6\" class=\"data row59 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row60_col0\" class=\"data row60 col0\" >validmind.data_validation.nlp.PolarityAndSubjectivity</td>\n",
              "      <td id=\"T_87ae0_row60_col1\" class=\"data row60 col1\" >Polarity And Subjectivity</td>\n",
              "      <td id=\"T_87ae0_row60_col2\" class=\"data row60 col2\" >Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution....</td>\n",
              "      <td id=\"T_87ae0_row60_col3\" class=\"data row60 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row60_col4\" class=\"data row60 col4\" >{'threshold_subjectivity': {'type': '_empty', 'default': 0.5}, 'threshold_polarity': {'type': '_empty', 'default': 0}}</td>\n",
              "      <td id=\"T_87ae0_row60_col5\" class=\"data row60 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
              "      <td id=\"T_87ae0_row60_col6\" class=\"data row60 col6\" >['nlp']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row61_col0\" class=\"data row61 col0\" >validmind.data_validation.nlp.Punctuations</td>\n",
              "      <td id=\"T_87ae0_row61_col1\" class=\"data row61 col1\" >Punctuations</td>\n",
              "      <td id=\"T_87ae0_row61_col2\" class=\"data row61 col2\" >Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....</td>\n",
              "      <td id=\"T_87ae0_row61_col3\" class=\"data row61 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row61_col4\" class=\"data row61 col4\" >{'count_mode': {'type': '_empty', 'default': 'token'}}</td>\n",
              "      <td id=\"T_87ae0_row61_col5\" class=\"data row61 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
              "      <td id=\"T_87ae0_row61_col6\" class=\"data row61 col6\" >['text_classification', 'text_summarization', 'nlp']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row62_col0\" class=\"data row62 col0\" >validmind.data_validation.nlp.Sentiment</td>\n",
              "      <td id=\"T_87ae0_row62_col1\" class=\"data row62 col1\" >Sentiment</td>\n",
              "      <td id=\"T_87ae0_row62_col2\" class=\"data row62 col2\" >Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....</td>\n",
              "      <td id=\"T_87ae0_row62_col3\" class=\"data row62 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row62_col4\" class=\"data row62 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row62_col5\" class=\"data row62 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
              "      <td id=\"T_87ae0_row62_col6\" class=\"data row62 col6\" >['nlp']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row63_col0\" class=\"data row63 col0\" >validmind.data_validation.nlp.StopWords</td>\n",
              "      <td id=\"T_87ae0_row63_col1\" class=\"data row63 col1\" >Stop Words</td>\n",
              "      <td id=\"T_87ae0_row63_col2\" class=\"data row63 col2\" >Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....</td>\n",
              "      <td id=\"T_87ae0_row63_col3\" class=\"data row63 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row63_col4\" class=\"data row63 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 0.5}, 'num_words': {'type': 'int', 'default': 25}}</td>\n",
              "      <td id=\"T_87ae0_row63_col5\" class=\"data row63 col5\" >['nlp', 'text_data', 'frequency_analysis', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row63_col6\" class=\"data row63 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row64_col0\" class=\"data row64 col0\" >validmind.data_validation.nlp.TextDescription</td>\n",
              "      <td id=\"T_87ae0_row64_col1\" class=\"data row64 col1\" >Text Description</td>\n",
              "      <td id=\"T_87ae0_row64_col2\" class=\"data row64 col2\" >Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate...</td>\n",
              "      <td id=\"T_87ae0_row64_col3\" class=\"data row64 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row64_col4\" class=\"data row64 col4\" >{'unwanted_tokens': {'type': 'set', 'default': {\"s'\", \"'s\", ' ', 'mr', \"''\", 'dollar', 'dr', 'mrs', '``', 's', 'us', 'ms'}}, 'lang': {'type': 'str', 'default': 'english'}}</td>\n",
              "      <td id=\"T_87ae0_row64_col5\" class=\"data row64 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row64_col6\" class=\"data row64 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row65_col0\" class=\"data row65 col0\" >validmind.data_validation.nlp.Toxicity</td>\n",
              "      <td id=\"T_87ae0_row65_col1\" class=\"data row65 col1\" >Toxicity</td>\n",
              "      <td id=\"T_87ae0_row65_col2\" class=\"data row65 col2\" >Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores....</td>\n",
              "      <td id=\"T_87ae0_row65_col3\" class=\"data row65 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row65_col4\" class=\"data row65 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row65_col5\" class=\"data row65 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
              "      <td id=\"T_87ae0_row65_col6\" class=\"data row65 col6\" >['nlp']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row66_col0\" class=\"data row66 col0\" >validmind.model_validation.BertScore</td>\n",
              "      <td id=\"T_87ae0_row66_col1\" class=\"data row66 col1\" >Bert Score</td>\n",
              "      <td id=\"T_87ae0_row66_col2\" class=\"data row66 col2\" >Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms...</td>\n",
              "      <td id=\"T_87ae0_row66_col3\" class=\"data row66 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row66_col4\" class=\"data row66 col4\" >{'evaluation_model': {'type': '_empty', 'default': 'distilbert-base-uncased'}}</td>\n",
              "      <td id=\"T_87ae0_row66_col5\" class=\"data row66 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row66_col6\" class=\"data row66 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row67_col0\" class=\"data row67 col0\" >validmind.model_validation.BleuScore</td>\n",
              "      <td id=\"T_87ae0_row67_col1\" class=\"data row67 col1\" >Bleu Score</td>\n",
              "      <td id=\"T_87ae0_row67_col2\" class=\"data row67 col2\" >Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...</td>\n",
              "      <td id=\"T_87ae0_row67_col3\" class=\"data row67 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row67_col4\" class=\"data row67 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row67_col5\" class=\"data row67 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row67_col6\" class=\"data row67 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row68_col0\" class=\"data row68 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
              "      <td id=\"T_87ae0_row68_col1\" class=\"data row68 col1\" >Cluster Size Distribution</td>\n",
              "      <td id=\"T_87ae0_row68_col2\" class=\"data row68 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
              "      <td id=\"T_87ae0_row68_col3\" class=\"data row68 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row68_col4\" class=\"data row68 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row68_col5\" class=\"data row68 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row68_col6\" class=\"data row68 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row69_col0\" class=\"data row69 col0\" >validmind.model_validation.ContextualRecall</td>\n",
              "      <td id=\"T_87ae0_row69_col1\" class=\"data row69 col1\" >Contextual Recall</td>\n",
              "      <td id=\"T_87ae0_row69_col2\" class=\"data row69 col2\" >Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct...</td>\n",
              "      <td id=\"T_87ae0_row69_col3\" class=\"data row69 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row69_col4\" class=\"data row69 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row69_col5\" class=\"data row69 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row69_col6\" class=\"data row69 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row70_col0\" class=\"data row70 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
              "      <td id=\"T_87ae0_row70_col1\" class=\"data row70 col1\" >Features AUC</td>\n",
              "      <td id=\"T_87ae0_row70_col2\" class=\"data row70 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
              "      <td id=\"T_87ae0_row70_col3\" class=\"data row70 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row70_col4\" class=\"data row70 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
              "      <td id=\"T_87ae0_row70_col5\" class=\"data row70 col5\" >['feature_importance', 'AUC', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row70_col6\" class=\"data row70 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row71_col0\" class=\"data row71 col0\" >validmind.model_validation.MeteorScore</td>\n",
              "      <td id=\"T_87ae0_row71_col1\" class=\"data row71 col1\" >Meteor Score</td>\n",
              "      <td id=\"T_87ae0_row71_col2\" class=\"data row71 col2\" >Assesses the quality of machine-generated translations by comparing them to human-produced references using the...</td>\n",
              "      <td id=\"T_87ae0_row71_col3\" class=\"data row71 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row71_col4\" class=\"data row71 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row71_col5\" class=\"data row71 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row71_col6\" class=\"data row71 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row72_col0\" class=\"data row72 col0\" >validmind.model_validation.ModelMetadata</td>\n",
              "      <td id=\"T_87ae0_row72_col1\" class=\"data row72 col1\" >Model Metadata</td>\n",
              "      <td id=\"T_87ae0_row72_col2\" class=\"data row72 col2\" >Compare metadata of different models and generate a summary table with the results....</td>\n",
              "      <td id=\"T_87ae0_row72_col3\" class=\"data row72 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row72_col4\" class=\"data row72 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row72_col5\" class=\"data row72 col5\" >['model_training', 'metadata']</td>\n",
              "      <td id=\"T_87ae0_row72_col6\" class=\"data row72 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row73_col0\" class=\"data row73 col0\" >validmind.model_validation.ModelPredictionResiduals</td>\n",
              "      <td id=\"T_87ae0_row73_col1\" class=\"data row73 col1\" >Model Prediction Residuals</td>\n",
              "      <td id=\"T_87ae0_row73_col2\" class=\"data row73 col2\" >Assesses normality and behavior of residuals in regression models through visualization and statistical tests....</td>\n",
              "      <td id=\"T_87ae0_row73_col3\" class=\"data row73 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row73_col4\" class=\"data row73 col4\" >{'nbins': {'type': '_empty', 'default': 100}, 'p_value_threshold': {'type': '_empty', 'default': 0.05}, 'start_date': {'type': '_empty', 'default': None}, 'end_date': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row73_col5\" class=\"data row73 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row73_col6\" class=\"data row73 col6\" >['residual_analysis', 'visualization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row74_col0\" class=\"data row74 col0\" >validmind.model_validation.RegardScore</td>\n",
              "      <td id=\"T_87ae0_row74_col1\" class=\"data row74 col1\" >Regard Score</td>\n",
              "      <td id=\"T_87ae0_row74_col2\" class=\"data row74 col2\" >Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard...</td>\n",
              "      <td id=\"T_87ae0_row74_col3\" class=\"data row74 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row74_col4\" class=\"data row74 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row74_col5\" class=\"data row74 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row74_col6\" class=\"data row74 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row75_col0\" class=\"data row75 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
              "      <td id=\"T_87ae0_row75_col1\" class=\"data row75 col1\" >Regression Residuals Plot</td>\n",
              "      <td id=\"T_87ae0_row75_col2\" class=\"data row75 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
              "      <td id=\"T_87ae0_row75_col3\" class=\"data row75 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row75_col4\" class=\"data row75 col4\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_87ae0_row75_col5\" class=\"data row75 col5\" >['model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row75_col6\" class=\"data row75 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row76_col0\" class=\"data row76 col0\" >validmind.model_validation.RougeScore</td>\n",
              "      <td id=\"T_87ae0_row76_col1\" class=\"data row76 col1\" >Rouge Score</td>\n",
              "      <td id=\"T_87ae0_row76_col2\" class=\"data row76 col2\" >Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide...</td>\n",
              "      <td id=\"T_87ae0_row76_col3\" class=\"data row76 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row76_col4\" class=\"data row76 col4\" >{'metric': {'type': '_empty', 'default': 'rouge-1'}}</td>\n",
              "      <td id=\"T_87ae0_row76_col5\" class=\"data row76 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row76_col6\" class=\"data row76 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row77_col0\" class=\"data row77 col0\" >validmind.model_validation.TimeSeriesPredictionWithCI</td>\n",
              "      <td id=\"T_87ae0_row77_col1\" class=\"data row77 col1\" >Time Series Prediction With CI</td>\n",
              "      <td id=\"T_87ae0_row77_col2\" class=\"data row77 col2\" >Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence...</td>\n",
              "      <td id=\"T_87ae0_row77_col3\" class=\"data row77 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row77_col4\" class=\"data row77 col4\" >{'confidence': {'type': '_empty', 'default': 0.95}}</td>\n",
              "      <td id=\"T_87ae0_row77_col5\" class=\"data row77 col5\" >['model_predictions', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row77_col6\" class=\"data row77 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row78_col0\" class=\"data row78 col0\" >validmind.model_validation.TimeSeriesPredictionsPlot</td>\n",
              "      <td id=\"T_87ae0_row78_col1\" class=\"data row78 col1\" >Time Series Predictions Plot</td>\n",
              "      <td id=\"T_87ae0_row78_col2\" class=\"data row78 col2\" >Plot actual vs predicted values for time series data and generate a visual comparison for the model....</td>\n",
              "      <td id=\"T_87ae0_row78_col3\" class=\"data row78 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row78_col4\" class=\"data row78 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row78_col5\" class=\"data row78 col5\" >['model_predictions', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row78_col6\" class=\"data row78 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row79_col0\" class=\"data row79 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
              "      <td id=\"T_87ae0_row79_col1\" class=\"data row79 col1\" >Time Series R2 Square By Segments</td>\n",
              "      <td id=\"T_87ae0_row79_col2\" class=\"data row79 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
              "      <td id=\"T_87ae0_row79_col3\" class=\"data row79 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row79_col4\" class=\"data row79 col4\" >{'segments': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row79_col5\" class=\"data row79 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_87ae0_row79_col6\" class=\"data row79 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row80_col0\" class=\"data row80 col0\" >validmind.model_validation.TokenDisparity</td>\n",
              "      <td id=\"T_87ae0_row80_col1\" class=\"data row80 col1\" >Token Disparity</td>\n",
              "      <td id=\"T_87ae0_row80_col2\" class=\"data row80 col2\" >Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and...</td>\n",
              "      <td id=\"T_87ae0_row80_col3\" class=\"data row80 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row80_col4\" class=\"data row80 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row80_col5\" class=\"data row80 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row80_col6\" class=\"data row80 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row81_col0\" class=\"data row81 col0\" >validmind.model_validation.ToxicityScore</td>\n",
              "      <td id=\"T_87ae0_row81_col1\" class=\"data row81 col1\" >Toxicity Score</td>\n",
              "      <td id=\"T_87ae0_row81_col2\" class=\"data row81 col2\" >Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content....</td>\n",
              "      <td id=\"T_87ae0_row81_col3\" class=\"data row81 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row81_col4\" class=\"data row81 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row81_col5\" class=\"data row81 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row81_col6\" class=\"data row81 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row82_col0\" class=\"data row82 col0\" >validmind.model_validation.embeddings.ClusterDistribution</td>\n",
              "      <td id=\"T_87ae0_row82_col1\" class=\"data row82 col1\" >Cluster Distribution</td>\n",
              "      <td id=\"T_87ae0_row82_col2\" class=\"data row82 col2\" >Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....</td>\n",
              "      <td id=\"T_87ae0_row82_col3\" class=\"data row82 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row82_col4\" class=\"data row82 col4\" >{'num_clusters': {'type': 'int', 'default': 5}}</td>\n",
              "      <td id=\"T_87ae0_row82_col5\" class=\"data row82 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row82_col6\" class=\"data row82 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row83_col0\" class=\"data row83 col0\" >validmind.model_validation.embeddings.CosineSimilarityComparison</td>\n",
              "      <td id=\"T_87ae0_row83_col1\" class=\"data row83 col1\" >Cosine Similarity Comparison</td>\n",
              "      <td id=\"T_87ae0_row83_col2\" class=\"data row83 col2\" >Assesses the similarity between embeddings generated by different models using Cosine Similarity, providing both...</td>\n",
              "      <td id=\"T_87ae0_row83_col3\" class=\"data row83 col3\" >['dataset', 'models']</td>\n",
              "      <td id=\"T_87ae0_row83_col4\" class=\"data row83 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row83_col5\" class=\"data row83 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row83_col6\" class=\"data row83 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row84_col0\" class=\"data row84 col0\" >validmind.model_validation.embeddings.CosineSimilarityDistribution</td>\n",
              "      <td id=\"T_87ae0_row84_col1\" class=\"data row84 col1\" >Cosine Similarity Distribution</td>\n",
              "      <td id=\"T_87ae0_row84_col2\" class=\"data row84 col2\" >Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...</td>\n",
              "      <td id=\"T_87ae0_row84_col3\" class=\"data row84 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row84_col4\" class=\"data row84 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row84_col5\" class=\"data row84 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row84_col6\" class=\"data row84 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row85_col0\" class=\"data row85 col0\" >validmind.model_validation.embeddings.CosineSimilarityHeatmap</td>\n",
              "      <td id=\"T_87ae0_row85_col1\" class=\"data row85 col1\" >Cosine Similarity Heatmap</td>\n",
              "      <td id=\"T_87ae0_row85_col2\" class=\"data row85 col2\" >Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....</td>\n",
              "      <td id=\"T_87ae0_row85_col3\" class=\"data row85 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row85_col4\" class=\"data row85 col4\" >{'title': {'type': '_empty', 'default': 'Cosine Similarity Matrix'}, 'color': {'type': '_empty', 'default': 'Cosine Similarity'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
              "      <td id=\"T_87ae0_row85_col5\" class=\"data row85 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row85_col6\" class=\"data row85 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row86_col0\" class=\"data row86 col0\" >validmind.model_validation.embeddings.DescriptiveAnalytics</td>\n",
              "      <td id=\"T_87ae0_row86_col1\" class=\"data row86 col1\" >Descriptive Analytics</td>\n",
              "      <td id=\"T_87ae0_row86_col2\" class=\"data row86 col2\" >Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...</td>\n",
              "      <td id=\"T_87ae0_row86_col3\" class=\"data row86 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row86_col4\" class=\"data row86 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row86_col5\" class=\"data row86 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row86_col6\" class=\"data row86 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row87_col0\" class=\"data row87 col0\" >validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
              "      <td id=\"T_87ae0_row87_col1\" class=\"data row87 col1\" >Embeddings Visualization2 D</td>\n",
              "      <td id=\"T_87ae0_row87_col2\" class=\"data row87 col2\" >Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....</td>\n",
              "      <td id=\"T_87ae0_row87_col3\" class=\"data row87 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row87_col4\" class=\"data row87 col4\" >{'cluster_column': {'type': None, 'default': None}, 'perplexity': {'type': 'int', 'default': 30}}</td>\n",
              "      <td id=\"T_87ae0_row87_col5\" class=\"data row87 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row87_col6\" class=\"data row87 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row88_col0\" class=\"data row88 col0\" >validmind.model_validation.embeddings.EuclideanDistanceComparison</td>\n",
              "      <td id=\"T_87ae0_row88_col1\" class=\"data row88 col1\" >Euclidean Distance Comparison</td>\n",
              "      <td id=\"T_87ae0_row88_col2\" class=\"data row88 col2\" >Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights...</td>\n",
              "      <td id=\"T_87ae0_row88_col3\" class=\"data row88 col3\" >['dataset', 'models']</td>\n",
              "      <td id=\"T_87ae0_row88_col4\" class=\"data row88 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row88_col5\" class=\"data row88 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row88_col6\" class=\"data row88 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row89_col0\" class=\"data row89 col0\" >validmind.model_validation.embeddings.EuclideanDistanceHeatmap</td>\n",
              "      <td id=\"T_87ae0_row89_col1\" class=\"data row89 col1\" >Euclidean Distance Heatmap</td>\n",
              "      <td id=\"T_87ae0_row89_col2\" class=\"data row89 col2\" >Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....</td>\n",
              "      <td id=\"T_87ae0_row89_col3\" class=\"data row89 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row89_col4\" class=\"data row89 col4\" >{'title': {'type': '_empty', 'default': 'Euclidean Distance Matrix'}, 'color': {'type': '_empty', 'default': 'Euclidean Distance'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
              "      <td id=\"T_87ae0_row89_col5\" class=\"data row89 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row89_col6\" class=\"data row89 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row90_col0\" class=\"data row90 col0\" >validmind.model_validation.embeddings.PCAComponentsPairwisePlots</td>\n",
              "      <td id=\"T_87ae0_row90_col1\" class=\"data row90 col1\" >PCA Components Pairwise Plots</td>\n",
              "      <td id=\"T_87ae0_row90_col2\" class=\"data row90 col2\" >Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model...</td>\n",
              "      <td id=\"T_87ae0_row90_col3\" class=\"data row90 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row90_col4\" class=\"data row90 col4\" >{'n_components': {'type': '_empty', 'default': 3}}</td>\n",
              "      <td id=\"T_87ae0_row90_col5\" class=\"data row90 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row90_col6\" class=\"data row90 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row91_col0\" class=\"data row91 col0\" >validmind.model_validation.embeddings.StabilityAnalysisKeyword</td>\n",
              "      <td id=\"T_87ae0_row91_col1\" class=\"data row91 col1\" >Stability Analysis Keyword</td>\n",
              "      <td id=\"T_87ae0_row91_col2\" class=\"data row91 col2\" >Evaluates robustness of embedding models to keyword swaps in the test dataset....</td>\n",
              "      <td id=\"T_87ae0_row91_col3\" class=\"data row91 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row91_col4\" class=\"data row91 col4\" >{'keyword_dict': {'type': None, 'default': None}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_87ae0_row91_col5\" class=\"data row91 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row91_col6\" class=\"data row91 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row92_col0\" class=\"data row92 col0\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise</td>\n",
              "      <td id=\"T_87ae0_row92_col1\" class=\"data row92 col1\" >Stability Analysis Random Noise</td>\n",
              "      <td id=\"T_87ae0_row92_col2\" class=\"data row92 col2\" >Assesses the robustness of text embeddings models to random noise introduced via text perturbations....</td>\n",
              "      <td id=\"T_87ae0_row92_col3\" class=\"data row92 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row92_col4\" class=\"data row92 col4\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_87ae0_row92_col5\" class=\"data row92 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row92_col6\" class=\"data row92 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row93_col0\" class=\"data row93 col0\" >validmind.model_validation.embeddings.StabilityAnalysisSynonyms</td>\n",
              "      <td id=\"T_87ae0_row93_col1\" class=\"data row93 col1\" >Stability Analysis Synonyms</td>\n",
              "      <td id=\"T_87ae0_row93_col2\" class=\"data row93 col2\" >Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....</td>\n",
              "      <td id=\"T_87ae0_row93_col3\" class=\"data row93 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row93_col4\" class=\"data row93 col4\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_87ae0_row93_col5\" class=\"data row93 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row93_col6\" class=\"data row93 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row94_col0\" class=\"data row94 col0\" >validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
              "      <td id=\"T_87ae0_row94_col1\" class=\"data row94 col1\" >Stability Analysis Translation</td>\n",
              "      <td id=\"T_87ae0_row94_col2\" class=\"data row94 col2\" >Evaluates robustness of text embeddings models to noise introduced by translating the original text to another...</td>\n",
              "      <td id=\"T_87ae0_row94_col3\" class=\"data row94 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row94_col4\" class=\"data row94 col4\" >{'source_lang': {'type': 'str', 'default': 'en'}, 'target_lang': {'type': 'str', 'default': 'fr'}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_87ae0_row94_col5\" class=\"data row94 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row94_col6\" class=\"data row94 col6\" >['feature_extraction']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row95_col0\" class=\"data row95 col0\" >validmind.model_validation.embeddings.TSNEComponentsPairwisePlots</td>\n",
              "      <td id=\"T_87ae0_row95_col1\" class=\"data row95 col1\" >TSNE Components Pairwise Plots</td>\n",
              "      <td id=\"T_87ae0_row95_col2\" class=\"data row95 col2\" >Creates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential...</td>\n",
              "      <td id=\"T_87ae0_row95_col3\" class=\"data row95 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row95_col4\" class=\"data row95 col4\" >{'n_components': {'type': '_empty', 'default': 2}, 'perplexity': {'type': '_empty', 'default': 30}, 'title': {'type': '_empty', 'default': 't-SNE'}}</td>\n",
              "      <td id=\"T_87ae0_row95_col5\" class=\"data row95 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
              "      <td id=\"T_87ae0_row95_col6\" class=\"data row95 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row96_col0\" class=\"data row96 col0\" >validmind.model_validation.ragas.AnswerCorrectness</td>\n",
              "      <td id=\"T_87ae0_row96_col1\" class=\"data row96 col1\" >Answer Correctness</td>\n",
              "      <td id=\"T_87ae0_row96_col2\" class=\"data row96 col2\" >Evaluates the correctness of answers in a dataset with respect to the provided ground...</td>\n",
              "      <td id=\"T_87ae0_row96_col3\" class=\"data row96 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row96_col4\" class=\"data row96 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}</td>\n",
              "      <td id=\"T_87ae0_row96_col5\" class=\"data row96 col5\" >['ragas', 'llm']</td>\n",
              "      <td id=\"T_87ae0_row96_col6\" class=\"data row96 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row97_col0\" class=\"data row97 col0\" >validmind.model_validation.ragas.AspectCritic</td>\n",
              "      <td id=\"T_87ae0_row97_col1\" class=\"data row97 col1\" >Aspect Critic</td>\n",
              "      <td id=\"T_87ae0_row97_col2\" class=\"data row97 col2\" >Evaluates generations against the following aspects: harmfulness, maliciousness,...</td>\n",
              "      <td id=\"T_87ae0_row97_col3\" class=\"data row97 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row97_col4\" class=\"data row97 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'aspects': {'type': 'list', 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row97_col5\" class=\"data row97 col5\" >['ragas', 'llm', 'qualitative']</td>\n",
              "      <td id=\"T_87ae0_row97_col6\" class=\"data row97 col6\" >['text_summarization', 'text_generation', 'text_qa']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row98_col0\" class=\"data row98 col0\" >validmind.model_validation.ragas.ContextEntityRecall</td>\n",
              "      <td id=\"T_87ae0_row98_col1\" class=\"data row98 col1\" >Context Entity Recall</td>\n",
              "      <td id=\"T_87ae0_row98_col2\" class=\"data row98 col2\" >Evaluates the context entity recall for dataset entries and visualizes the results....</td>\n",
              "      <td id=\"T_87ae0_row98_col3\" class=\"data row98 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row98_col4\" class=\"data row98 col4\" >{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
              "      <td id=\"T_87ae0_row98_col5\" class=\"data row98 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
              "      <td id=\"T_87ae0_row98_col6\" class=\"data row98 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row99_col0\" class=\"data row99 col0\" >validmind.model_validation.ragas.ContextPrecision</td>\n",
              "      <td id=\"T_87ae0_row99_col1\" class=\"data row99 col1\" >Context Precision</td>\n",
              "      <td id=\"T_87ae0_row99_col2\" class=\"data row99 col2\" >Context Precision is a metric that evaluates whether all of the ground-truth...</td>\n",
              "      <td id=\"T_87ae0_row99_col3\" class=\"data row99 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row99_col4\" class=\"data row99 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
              "      <td id=\"T_87ae0_row99_col5\" class=\"data row99 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
              "      <td id=\"T_87ae0_row99_col6\" class=\"data row99 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row100_col0\" class=\"data row100 col0\" >validmind.model_validation.ragas.ContextPrecisionWithoutReference</td>\n",
              "      <td id=\"T_87ae0_row100_col1\" class=\"data row100 col1\" >Context Precision Without Reference</td>\n",
              "      <td id=\"T_87ae0_row100_col2\" class=\"data row100 col2\" >Context Precision Without Reference is a metric used to evaluate the relevance of...</td>\n",
              "      <td id=\"T_87ae0_row100_col3\" class=\"data row100 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row100_col4\" class=\"data row100 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}}</td>\n",
              "      <td id=\"T_87ae0_row100_col5\" class=\"data row100 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
              "      <td id=\"T_87ae0_row100_col6\" class=\"data row100 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row101_col0\" class=\"data row101 col0\" >validmind.model_validation.ragas.ContextRecall</td>\n",
              "      <td id=\"T_87ae0_row101_col1\" class=\"data row101 col1\" >Context Recall</td>\n",
              "      <td id=\"T_87ae0_row101_col2\" class=\"data row101 col2\" >Context recall measures the extent to which the retrieved context aligns with the...</td>\n",
              "      <td id=\"T_87ae0_row101_col3\" class=\"data row101 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row101_col4\" class=\"data row101 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
              "      <td id=\"T_87ae0_row101_col5\" class=\"data row101 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
              "      <td id=\"T_87ae0_row101_col6\" class=\"data row101 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row102_col0\" class=\"data row102 col0\" >validmind.model_validation.ragas.Faithfulness</td>\n",
              "      <td id=\"T_87ae0_row102_col1\" class=\"data row102 col1\" >Faithfulness</td>\n",
              "      <td id=\"T_87ae0_row102_col2\" class=\"data row102 col2\" >Evaluates the faithfulness of the generated answers with respect to retrieved contexts....</td>\n",
              "      <td id=\"T_87ae0_row102_col3\" class=\"data row102 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row102_col4\" class=\"data row102 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}}</td>\n",
              "      <td id=\"T_87ae0_row102_col5\" class=\"data row102 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
              "      <td id=\"T_87ae0_row102_col6\" class=\"data row102 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row103_col0\" class=\"data row103 col0\" >validmind.model_validation.ragas.NoiseSensitivity</td>\n",
              "      <td id=\"T_87ae0_row103_col1\" class=\"data row103 col1\" >Noise Sensitivity</td>\n",
              "      <td id=\"T_87ae0_row103_col2\" class=\"data row103 col2\" >Assesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it...</td>\n",
              "      <td id=\"T_87ae0_row103_col3\" class=\"data row103 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row103_col4\" class=\"data row103 col4\" >{'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}, 'reference_column': {'type': '_empty', 'default': 'reference'}, 'focus': {'type': '_empty', 'default': 'relevant'}, 'user_input_column': {'type': '_empty', 'default': 'user_input'}}</td>\n",
              "      <td id=\"T_87ae0_row103_col5\" class=\"data row103 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
              "      <td id=\"T_87ae0_row103_col6\" class=\"data row103 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row104_col0\" class=\"data row104 col0\" >validmind.model_validation.ragas.ResponseRelevancy</td>\n",
              "      <td id=\"T_87ae0_row104_col1\" class=\"data row104 col1\" >Response Relevancy</td>\n",
              "      <td id=\"T_87ae0_row104_col2\" class=\"data row104 col2\" >Assesses how pertinent the generated answer is to the given prompt....</td>\n",
              "      <td id=\"T_87ae0_row104_col3\" class=\"data row104 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row104_col4\" class=\"data row104 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'response_column': {'type': '_empty', 'default': 'response'}}</td>\n",
              "      <td id=\"T_87ae0_row104_col5\" class=\"data row104 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
              "      <td id=\"T_87ae0_row104_col6\" class=\"data row104 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row105_col0\" class=\"data row105 col0\" >validmind.model_validation.ragas.SemanticSimilarity</td>\n",
              "      <td id=\"T_87ae0_row105_col1\" class=\"data row105 col1\" >Semantic Similarity</td>\n",
              "      <td id=\"T_87ae0_row105_col2\" class=\"data row105 col2\" >Calculates the semantic similarity between generated responses and ground truths...</td>\n",
              "      <td id=\"T_87ae0_row105_col3\" class=\"data row105 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row105_col4\" class=\"data row105 col4\" >{'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}</td>\n",
              "      <td id=\"T_87ae0_row105_col5\" class=\"data row105 col5\" >['ragas', 'llm']</td>\n",
              "      <td id=\"T_87ae0_row105_col6\" class=\"data row105 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row106_col0\" class=\"data row106 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
              "      <td id=\"T_87ae0_row106_col1\" class=\"data row106 col1\" >Adjusted Mutual Information</td>\n",
              "      <td id=\"T_87ae0_row106_col2\" class=\"data row106 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
              "      <td id=\"T_87ae0_row106_col3\" class=\"data row106 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row106_col4\" class=\"data row106 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row106_col5\" class=\"data row106 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_87ae0_row106_col6\" class=\"data row106 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row107_col0\" class=\"data row107 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
              "      <td id=\"T_87ae0_row107_col1\" class=\"data row107 col1\" >Adjusted Rand Index</td>\n",
              "      <td id=\"T_87ae0_row107_col2\" class=\"data row107 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
              "      <td id=\"T_87ae0_row107_col3\" class=\"data row107 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row107_col4\" class=\"data row107 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row107_col5\" class=\"data row107 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_87ae0_row107_col6\" class=\"data row107 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row108_col0\" class=\"data row108 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
              "      <td id=\"T_87ae0_row108_col1\" class=\"data row108 col1\" >Calibration Curve</td>\n",
              "      <td id=\"T_87ae0_row108_col2\" class=\"data row108 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
              "      <td id=\"T_87ae0_row108_col3\" class=\"data row108 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row108_col4\" class=\"data row108 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_87ae0_row108_col5\" class=\"data row108 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
              "      <td id=\"T_87ae0_row108_col6\" class=\"data row108 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row109_col0\" class=\"data row109 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
              "      <td id=\"T_87ae0_row109_col1\" class=\"data row109 col1\" >Classifier Performance</td>\n",
              "      <td id=\"T_87ae0_row109_col2\" class=\"data row109 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
              "      <td id=\"T_87ae0_row109_col3\" class=\"data row109 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row109_col4\" class=\"data row109 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
              "      <td id=\"T_87ae0_row109_col5\" class=\"data row109 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row109_col6\" class=\"data row109 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row110_col0\" class=\"data row110 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
              "      <td id=\"T_87ae0_row110_col1\" class=\"data row110 col1\" >Classifier Threshold Optimization</td>\n",
              "      <td id=\"T_87ae0_row110_col2\" class=\"data row110 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
              "      <td id=\"T_87ae0_row110_col3\" class=\"data row110 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row110_col4\" class=\"data row110 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row110_col5\" class=\"data row110 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
              "      <td id=\"T_87ae0_row110_col6\" class=\"data row110 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row111_col0\" class=\"data row111 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
              "      <td id=\"T_87ae0_row111_col1\" class=\"data row111 col1\" >Cluster Cosine Similarity</td>\n",
              "      <td id=\"T_87ae0_row111_col2\" class=\"data row111 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
              "      <td id=\"T_87ae0_row111_col3\" class=\"data row111 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row111_col4\" class=\"data row111 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row111_col5\" class=\"data row111 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_87ae0_row111_col6\" class=\"data row111 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row112_col0\" class=\"data row112 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
              "      <td id=\"T_87ae0_row112_col1\" class=\"data row112 col1\" >Cluster Performance Metrics</td>\n",
              "      <td id=\"T_87ae0_row112_col2\" class=\"data row112 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
              "      <td id=\"T_87ae0_row112_col3\" class=\"data row112 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row112_col4\" class=\"data row112 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row112_col5\" class=\"data row112 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_87ae0_row112_col6\" class=\"data row112 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row113_col0\" class=\"data row113 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
              "      <td id=\"T_87ae0_row113_col1\" class=\"data row113 col1\" >Completeness Score</td>\n",
              "      <td id=\"T_87ae0_row113_col2\" class=\"data row113 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
              "      <td id=\"T_87ae0_row113_col3\" class=\"data row113 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row113_col4\" class=\"data row113 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row113_col5\" class=\"data row113 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_87ae0_row113_col6\" class=\"data row113 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row114_col0\" class=\"data row114 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
              "      <td id=\"T_87ae0_row114_col1\" class=\"data row114 col1\" >Confusion Matrix</td>\n",
              "      <td id=\"T_87ae0_row114_col2\" class=\"data row114 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
              "      <td id=\"T_87ae0_row114_col3\" class=\"data row114 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row114_col4\" class=\"data row114 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_87ae0_row114_col5\" class=\"data row114 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row114_col6\" class=\"data row114 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row115_col0\" class=\"data row115 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
              "      <td id=\"T_87ae0_row115_col1\" class=\"data row115 col1\" >Feature Importance</td>\n",
              "      <td id=\"T_87ae0_row115_col2\" class=\"data row115 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
              "      <td id=\"T_87ae0_row115_col3\" class=\"data row115 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row115_col4\" class=\"data row115 col4\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
              "      <td id=\"T_87ae0_row115_col5\" class=\"data row115 col5\" >['model_explainability', 'sklearn']</td>\n",
              "      <td id=\"T_87ae0_row115_col6\" class=\"data row115 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row116_col0\" class=\"data row116 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
              "      <td id=\"T_87ae0_row116_col1\" class=\"data row116 col1\" >Fowlkes Mallows Score</td>\n",
              "      <td id=\"T_87ae0_row116_col2\" class=\"data row116 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
              "      <td id=\"T_87ae0_row116_col3\" class=\"data row116 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row116_col4\" class=\"data row116 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row116_col5\" class=\"data row116 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row116_col6\" class=\"data row116 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row117_col0\" class=\"data row117 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
              "      <td id=\"T_87ae0_row117_col1\" class=\"data row117 col1\" >Homogeneity Score</td>\n",
              "      <td id=\"T_87ae0_row117_col2\" class=\"data row117 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
              "      <td id=\"T_87ae0_row117_col3\" class=\"data row117 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row117_col4\" class=\"data row117 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row117_col5\" class=\"data row117 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row117_col6\" class=\"data row117 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row118_col0\" class=\"data row118 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
              "      <td id=\"T_87ae0_row118_col1\" class=\"data row118 col1\" >Hyper Parameters Tuning</td>\n",
              "      <td id=\"T_87ae0_row118_col2\" class=\"data row118 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
              "      <td id=\"T_87ae0_row118_col3\" class=\"data row118 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row118_col4\" class=\"data row118 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row118_col5\" class=\"data row118 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row118_col6\" class=\"data row118 col6\" >['clustering', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row119_col0\" class=\"data row119 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
              "      <td id=\"T_87ae0_row119_col1\" class=\"data row119 col1\" >K Means Clusters Optimization</td>\n",
              "      <td id=\"T_87ae0_row119_col2\" class=\"data row119 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
              "      <td id=\"T_87ae0_row119_col3\" class=\"data row119 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row119_col4\" class=\"data row119 col4\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row119_col5\" class=\"data row119 col5\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
              "      <td id=\"T_87ae0_row119_col6\" class=\"data row119 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row120_col0\" class=\"data row120 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
              "      <td id=\"T_87ae0_row120_col1\" class=\"data row120 col1\" >Minimum Accuracy</td>\n",
              "      <td id=\"T_87ae0_row120_col2\" class=\"data row120 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_87ae0_row120_col3\" class=\"data row120 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row120_col4\" class=\"data row120 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_87ae0_row120_col5\" class=\"data row120 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row120_col6\" class=\"data row120 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row121_col0\" class=\"data row121 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
              "      <td id=\"T_87ae0_row121_col1\" class=\"data row121 col1\" >Minimum F1 Score</td>\n",
              "      <td id=\"T_87ae0_row121_col2\" class=\"data row121 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
              "      <td id=\"T_87ae0_row121_col3\" class=\"data row121 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row121_col4\" class=\"data row121 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_87ae0_row121_col5\" class=\"data row121 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row121_col6\" class=\"data row121 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row122_col0\" class=\"data row122 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
              "      <td id=\"T_87ae0_row122_col1\" class=\"data row122 col1\" >Minimum ROCAUC Score</td>\n",
              "      <td id=\"T_87ae0_row122_col2\" class=\"data row122 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_87ae0_row122_col3\" class=\"data row122 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row122_col4\" class=\"data row122 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_87ae0_row122_col5\" class=\"data row122 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row122_col6\" class=\"data row122 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row123_col0\" class=\"data row123 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
              "      <td id=\"T_87ae0_row123_col1\" class=\"data row123 col1\" >Model Parameters</td>\n",
              "      <td id=\"T_87ae0_row123_col2\" class=\"data row123 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
              "      <td id=\"T_87ae0_row123_col3\" class=\"data row123 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row123_col4\" class=\"data row123 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row123_col5\" class=\"data row123 col5\" >['model_training', 'metadata']</td>\n",
              "      <td id=\"T_87ae0_row123_col6\" class=\"data row123 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row124_col0\" class=\"data row124 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
              "      <td id=\"T_87ae0_row124_col1\" class=\"data row124 col1\" >Models Performance Comparison</td>\n",
              "      <td id=\"T_87ae0_row124_col2\" class=\"data row124 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
              "      <td id=\"T_87ae0_row124_col3\" class=\"data row124 col3\" >['dataset', 'models']</td>\n",
              "      <td id=\"T_87ae0_row124_col4\" class=\"data row124 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row124_col5\" class=\"data row124 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
              "      <td id=\"T_87ae0_row124_col6\" class=\"data row124 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row125_col0\" class=\"data row125 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
              "      <td id=\"T_87ae0_row125_col1\" class=\"data row125 col1\" >Overfit Diagnosis</td>\n",
              "      <td id=\"T_87ae0_row125_col2\" class=\"data row125 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
              "      <td id=\"T_87ae0_row125_col3\" class=\"data row125 col3\" >['model', 'datasets']</td>\n",
              "      <td id=\"T_87ae0_row125_col4\" class=\"data row125 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
              "      <td id=\"T_87ae0_row125_col5\" class=\"data row125 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
              "      <td id=\"T_87ae0_row125_col6\" class=\"data row125 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row126_col0\" class=\"data row126 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
              "      <td id=\"T_87ae0_row126_col1\" class=\"data row126 col1\" >Permutation Feature Importance</td>\n",
              "      <td id=\"T_87ae0_row126_col2\" class=\"data row126 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
              "      <td id=\"T_87ae0_row126_col3\" class=\"data row126 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row126_col4\" class=\"data row126 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row126_col5\" class=\"data row126 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row126_col6\" class=\"data row126 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row127_col0\" class=\"data row127 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
              "      <td id=\"T_87ae0_row127_col1\" class=\"data row127 col1\" >Population Stability Index</td>\n",
              "      <td id=\"T_87ae0_row127_col2\" class=\"data row127 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
              "      <td id=\"T_87ae0_row127_col3\" class=\"data row127 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row127_col4\" class=\"data row127 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
              "      <td id=\"T_87ae0_row127_col5\" class=\"data row127 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row127_col6\" class=\"data row127 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row128_col0\" class=\"data row128 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
              "      <td id=\"T_87ae0_row128_col1\" class=\"data row128 col1\" >Precision Recall Curve</td>\n",
              "      <td id=\"T_87ae0_row128_col2\" class=\"data row128 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
              "      <td id=\"T_87ae0_row128_col3\" class=\"data row128 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row128_col4\" class=\"data row128 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row128_col5\" class=\"data row128 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row128_col6\" class=\"data row128 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row129_col0\" class=\"data row129 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
              "      <td id=\"T_87ae0_row129_col1\" class=\"data row129 col1\" >ROC Curve</td>\n",
              "      <td id=\"T_87ae0_row129_col2\" class=\"data row129 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
              "      <td id=\"T_87ae0_row129_col3\" class=\"data row129 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row129_col4\" class=\"data row129 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row129_col5\" class=\"data row129 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row129_col6\" class=\"data row129 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row130_col0\" class=\"data row130 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
              "      <td id=\"T_87ae0_row130_col1\" class=\"data row130 col1\" >Regression Errors</td>\n",
              "      <td id=\"T_87ae0_row130_col2\" class=\"data row130 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
              "      <td id=\"T_87ae0_row130_col3\" class=\"data row130 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row130_col4\" class=\"data row130 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row130_col5\" class=\"data row130 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row130_col6\" class=\"data row130 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row131_col0\" class=\"data row131 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
              "      <td id=\"T_87ae0_row131_col1\" class=\"data row131 col1\" >Regression Errors Comparison</td>\n",
              "      <td id=\"T_87ae0_row131_col2\" class=\"data row131 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
              "      <td id=\"T_87ae0_row131_col3\" class=\"data row131 col3\" >['datasets', 'models']</td>\n",
              "      <td id=\"T_87ae0_row131_col4\" class=\"data row131 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row131_col5\" class=\"data row131 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_87ae0_row131_col6\" class=\"data row131 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row132_col0\" class=\"data row132 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
              "      <td id=\"T_87ae0_row132_col1\" class=\"data row132 col1\" >Regression Performance</td>\n",
              "      <td id=\"T_87ae0_row132_col2\" class=\"data row132 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
              "      <td id=\"T_87ae0_row132_col3\" class=\"data row132 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row132_col4\" class=\"data row132 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row132_col5\" class=\"data row132 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row132_col6\" class=\"data row132 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row133_col0\" class=\"data row133 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
              "      <td id=\"T_87ae0_row133_col1\" class=\"data row133 col1\" >Regression R2 Square</td>\n",
              "      <td id=\"T_87ae0_row133_col2\" class=\"data row133 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
              "      <td id=\"T_87ae0_row133_col3\" class=\"data row133 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row133_col4\" class=\"data row133 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row133_col5\" class=\"data row133 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row133_col6\" class=\"data row133 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row134_col0\" class=\"data row134 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
              "      <td id=\"T_87ae0_row134_col1\" class=\"data row134 col1\" >Regression R2 Square Comparison</td>\n",
              "      <td id=\"T_87ae0_row134_col2\" class=\"data row134 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
              "      <td id=\"T_87ae0_row134_col3\" class=\"data row134 col3\" >['datasets', 'models']</td>\n",
              "      <td id=\"T_87ae0_row134_col4\" class=\"data row134 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row134_col5\" class=\"data row134 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_87ae0_row134_col6\" class=\"data row134 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row135_col0\" class=\"data row135 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
              "      <td id=\"T_87ae0_row135_col1\" class=\"data row135 col1\" >Robustness Diagnosis</td>\n",
              "      <td id=\"T_87ae0_row135_col2\" class=\"data row135 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
              "      <td id=\"T_87ae0_row135_col3\" class=\"data row135 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row135_col4\" class=\"data row135 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_87ae0_row135_col5\" class=\"data row135 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row135_col6\" class=\"data row135 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row136_col0\" class=\"data row136 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
              "      <td id=\"T_87ae0_row136_col1\" class=\"data row136 col1\" >SHAP Global Importance</td>\n",
              "      <td id=\"T_87ae0_row136_col2\" class=\"data row136 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
              "      <td id=\"T_87ae0_row136_col3\" class=\"data row136 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row136_col4\" class=\"data row136 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row136_col5\" class=\"data row136 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row136_col6\" class=\"data row136 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row137_col0\" class=\"data row137 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
              "      <td id=\"T_87ae0_row137_col1\" class=\"data row137 col1\" >Score Probability Alignment</td>\n",
              "      <td id=\"T_87ae0_row137_col2\" class=\"data row137 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
              "      <td id=\"T_87ae0_row137_col3\" class=\"data row137 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row137_col4\" class=\"data row137 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_87ae0_row137_col5\" class=\"data row137 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
              "      <td id=\"T_87ae0_row137_col6\" class=\"data row137 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row138_col0\" class=\"data row138 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
              "      <td id=\"T_87ae0_row138_col1\" class=\"data row138 col1\" >Silhouette Plot</td>\n",
              "      <td id=\"T_87ae0_row138_col2\" class=\"data row138 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
              "      <td id=\"T_87ae0_row138_col3\" class=\"data row138 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row138_col4\" class=\"data row138 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row138_col5\" class=\"data row138 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row138_col6\" class=\"data row138 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row139_col0\" class=\"data row139 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
              "      <td id=\"T_87ae0_row139_col1\" class=\"data row139 col1\" >Training Test Degradation</td>\n",
              "      <td id=\"T_87ae0_row139_col2\" class=\"data row139 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
              "      <td id=\"T_87ae0_row139_col3\" class=\"data row139 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row139_col4\" class=\"data row139 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_87ae0_row139_col5\" class=\"data row139 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row139_col6\" class=\"data row139 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row140_col0\" class=\"data row140 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
              "      <td id=\"T_87ae0_row140_col1\" class=\"data row140 col1\" >V Measure</td>\n",
              "      <td id=\"T_87ae0_row140_col2\" class=\"data row140 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
              "      <td id=\"T_87ae0_row140_col3\" class=\"data row140 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row140_col4\" class=\"data row140 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row140_col5\" class=\"data row140 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row140_col6\" class=\"data row140 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row141_col0\" class=\"data row141 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
              "      <td id=\"T_87ae0_row141_col1\" class=\"data row141 col1\" >Weakspots Diagnosis</td>\n",
              "      <td id=\"T_87ae0_row141_col2\" class=\"data row141 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
              "      <td id=\"T_87ae0_row141_col3\" class=\"data row141 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row141_col4\" class=\"data row141 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row141_col5\" class=\"data row141 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row141_col6\" class=\"data row141 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row142_col0\" class=\"data row142 col0\" >validmind.model_validation.statsmodels.AutoARIMA</td>\n",
              "      <td id=\"T_87ae0_row142_col1\" class=\"data row142 col1\" >Auto ARIMA</td>\n",
              "      <td id=\"T_87ae0_row142_col2\" class=\"data row142 col2\" >Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....</td>\n",
              "      <td id=\"T_87ae0_row142_col3\" class=\"data row142 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row142_col4\" class=\"data row142 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row142_col5\" class=\"data row142 col5\" >['time_series_data', 'forecasting', 'model_selection', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row142_col6\" class=\"data row142 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row143_col0\" class=\"data row143 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
              "      <td id=\"T_87ae0_row143_col1\" class=\"data row143 col1\" >Cumulative Prediction Probabilities</td>\n",
              "      <td id=\"T_87ae0_row143_col2\" class=\"data row143 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
              "      <td id=\"T_87ae0_row143_col3\" class=\"data row143 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row143_col4\" class=\"data row143 col4\" >{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}</td>\n",
              "      <td id=\"T_87ae0_row143_col5\" class=\"data row143 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_87ae0_row143_col6\" class=\"data row143 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row144_col0\" class=\"data row144 col0\" >validmind.model_validation.statsmodels.DurbinWatsonTest</td>\n",
              "      <td id=\"T_87ae0_row144_col1\" class=\"data row144 col1\" >Durbin Watson Test</td>\n",
              "      <td id=\"T_87ae0_row144_col2\" class=\"data row144 col2\" >Assesses autocorrelation in time series data features using the Durbin-Watson statistic....</td>\n",
              "      <td id=\"T_87ae0_row144_col3\" class=\"data row144 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row144_col4\" class=\"data row144 col4\" >{'threshold': {'type': '_empty', 'default': [1.5, 2.5]}}</td>\n",
              "      <td id=\"T_87ae0_row144_col5\" class=\"data row144 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row144_col6\" class=\"data row144 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row145_col0\" class=\"data row145 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
              "      <td id=\"T_87ae0_row145_col1\" class=\"data row145 col1\" >GINI Table</td>\n",
              "      <td id=\"T_87ae0_row145_col2\" class=\"data row145 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
              "      <td id=\"T_87ae0_row145_col3\" class=\"data row145 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row145_col4\" class=\"data row145 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row145_col5\" class=\"data row145 col5\" >['model_performance']</td>\n",
              "      <td id=\"T_87ae0_row145_col6\" class=\"data row145 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row146_col0\" class=\"data row146 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
              "      <td id=\"T_87ae0_row146_col1\" class=\"data row146 col1\" >Kolmogorov Smirnov</td>\n",
              "      <td id=\"T_87ae0_row146_col2\" class=\"data row146 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
              "      <td id=\"T_87ae0_row146_col3\" class=\"data row146 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row146_col4\" class=\"data row146 col4\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
              "      <td id=\"T_87ae0_row146_col5\" class=\"data row146 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row146_col6\" class=\"data row146 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row147_col0\" class=\"data row147 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
              "      <td id=\"T_87ae0_row147_col1\" class=\"data row147 col1\" >Lilliefors</td>\n",
              "      <td id=\"T_87ae0_row147_col2\" class=\"data row147 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
              "      <td id=\"T_87ae0_row147_col3\" class=\"data row147 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row147_col4\" class=\"data row147 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row147_col5\" class=\"data row147 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_87ae0_row147_col6\" class=\"data row147 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row148_col0\" class=\"data row148 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
              "      <td id=\"T_87ae0_row148_col1\" class=\"data row148 col1\" >Prediction Probabilities Histogram</td>\n",
              "      <td id=\"T_87ae0_row148_col2\" class=\"data row148 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
              "      <td id=\"T_87ae0_row148_col3\" class=\"data row148 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row148_col4\" class=\"data row148 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
              "      <td id=\"T_87ae0_row148_col5\" class=\"data row148 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_87ae0_row148_col6\" class=\"data row148 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row149_col0\" class=\"data row149 col0\" >validmind.model_validation.statsmodels.RegressionCoeffs</td>\n",
              "      <td id=\"T_87ae0_row149_col1\" class=\"data row149 col1\" >Regression Coeffs</td>\n",
              "      <td id=\"T_87ae0_row149_col2\" class=\"data row149 col2\" >Assesses the significance and uncertainty of predictor variables in a regression model through visualization of...</td>\n",
              "      <td id=\"T_87ae0_row149_col3\" class=\"data row149 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row149_col4\" class=\"data row149 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row149_col5\" class=\"data row149 col5\" >['tabular_data', 'visualization', 'model_training']</td>\n",
              "      <td id=\"T_87ae0_row149_col6\" class=\"data row149 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row150_col0\" class=\"data row150 col0\" >validmind.model_validation.statsmodels.RegressionFeatureSignificance</td>\n",
              "      <td id=\"T_87ae0_row150_col1\" class=\"data row150 col1\" >Regression Feature Significance</td>\n",
              "      <td id=\"T_87ae0_row150_col2\" class=\"data row150 col2\" >Assesses and visualizes the statistical significance of features in a regression model....</td>\n",
              "      <td id=\"T_87ae0_row150_col3\" class=\"data row150 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row150_col4\" class=\"data row150 col4\" >{'fontsize': {'type': 'int', 'default': 10}, 'p_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_87ae0_row150_col5\" class=\"data row150 col5\" >['statistical_test', 'model_interpretation', 'visualization', 'feature_importance']</td>\n",
              "      <td id=\"T_87ae0_row150_col6\" class=\"data row150 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row151_col0\" class=\"data row151 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlot</td>\n",
              "      <td id=\"T_87ae0_row151_col1\" class=\"data row151 col1\" >Regression Model Forecast Plot</td>\n",
              "      <td id=\"T_87ae0_row151_col2\" class=\"data row151 col2\" >Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over...</td>\n",
              "      <td id=\"T_87ae0_row151_col3\" class=\"data row151 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row151_col4\" class=\"data row151 col4\" >{'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row151_col5\" class=\"data row151 col5\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row151_col6\" class=\"data row151 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row152_col0\" class=\"data row152 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels</td>\n",
              "      <td id=\"T_87ae0_row152_col1\" class=\"data row152 col1\" >Regression Model Forecast Plot Levels</td>\n",
              "      <td id=\"T_87ae0_row152_col2\" class=\"data row152 col2\" >Assesses the alignment between forecasted and observed values in regression models through visual plots...</td>\n",
              "      <td id=\"T_87ae0_row152_col3\" class=\"data row152 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row152_col4\" class=\"data row152 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row152_col5\" class=\"data row152 col5\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row152_col6\" class=\"data row152 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row153_col0\" class=\"data row153 col0\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
              "      <td id=\"T_87ae0_row153_col1\" class=\"data row153 col1\" >Regression Model Sensitivity Plot</td>\n",
              "      <td id=\"T_87ae0_row153_col2\" class=\"data row153 col2\" >Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and...</td>\n",
              "      <td id=\"T_87ae0_row153_col3\" class=\"data row153 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row153_col4\" class=\"data row153 col4\" >{'shocks': {'type': None, 'default': [0.1]}, 'transformation': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_87ae0_row153_col5\" class=\"data row153 col5\" >['senstivity_analysis', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row153_col6\" class=\"data row153 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row154_col0\" class=\"data row154 col0\" >validmind.model_validation.statsmodels.RegressionModelSummary</td>\n",
              "      <td id=\"T_87ae0_row154_col1\" class=\"data row154 col1\" >Regression Model Summary</td>\n",
              "      <td id=\"T_87ae0_row154_col2\" class=\"data row154 col2\" >Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....</td>\n",
              "      <td id=\"T_87ae0_row154_col3\" class=\"data row154 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row154_col4\" class=\"data row154 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row154_col5\" class=\"data row154 col5\" >['model_performance', 'regression']</td>\n",
              "      <td id=\"T_87ae0_row154_col6\" class=\"data row154 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row155_col0\" class=\"data row155 col0\" >validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance</td>\n",
              "      <td id=\"T_87ae0_row155_col1\" class=\"data row155 col1\" >Regression Permutation Feature Importance</td>\n",
              "      <td id=\"T_87ae0_row155_col2\" class=\"data row155 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
              "      <td id=\"T_87ae0_row155_col3\" class=\"data row155 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row155_col4\" class=\"data row155 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
              "      <td id=\"T_87ae0_row155_col5\" class=\"data row155 col5\" >['statsmodels', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row155_col6\" class=\"data row155 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row156_col0\" class=\"data row156 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
              "      <td id=\"T_87ae0_row156_col1\" class=\"data row156 col1\" >Scorecard Histogram</td>\n",
              "      <td id=\"T_87ae0_row156_col2\" class=\"data row156 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
              "      <td id=\"T_87ae0_row156_col3\" class=\"data row156 col3\" >['dataset']</td>\n",
              "      <td id=\"T_87ae0_row156_col4\" class=\"data row156 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}</td>\n",
              "      <td id=\"T_87ae0_row156_col5\" class=\"data row156 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
              "      <td id=\"T_87ae0_row156_col6\" class=\"data row156 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row157_col0\" class=\"data row157 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
              "      <td id=\"T_87ae0_row157_col1\" class=\"data row157 col1\" >Calibration Curve Drift</td>\n",
              "      <td id=\"T_87ae0_row157_col2\" class=\"data row157 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row157_col3\" class=\"data row157 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row157_col4\" class=\"data row157 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row157_col5\" class=\"data row157 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row157_col6\" class=\"data row157 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row158_col0\" class=\"data row158 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
              "      <td id=\"T_87ae0_row158_col1\" class=\"data row158 col1\" >Class Discrimination Drift</td>\n",
              "      <td id=\"T_87ae0_row158_col2\" class=\"data row158 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row158_col3\" class=\"data row158 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row158_col4\" class=\"data row158 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row158_col5\" class=\"data row158 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row158_col6\" class=\"data row158 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row159_col0\" class=\"data row159 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
              "      <td id=\"T_87ae0_row159_col1\" class=\"data row159 col1\" >Class Imbalance Drift</td>\n",
              "      <td id=\"T_87ae0_row159_col2\" class=\"data row159 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row159_col3\" class=\"data row159 col3\" >['datasets']</td>\n",
              "      <td id=\"T_87ae0_row159_col4\" class=\"data row159 col4\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
              "      <td id=\"T_87ae0_row159_col5\" class=\"data row159 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
              "      <td id=\"T_87ae0_row159_col6\" class=\"data row159 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row160_col0\" class=\"data row160 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
              "      <td id=\"T_87ae0_row160_col1\" class=\"data row160 col1\" >Classification Accuracy Drift</td>\n",
              "      <td id=\"T_87ae0_row160_col2\" class=\"data row160 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row160_col3\" class=\"data row160 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row160_col4\" class=\"data row160 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row160_col5\" class=\"data row160 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row160_col6\" class=\"data row160 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row161_col0\" class=\"data row161 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
              "      <td id=\"T_87ae0_row161_col1\" class=\"data row161 col1\" >Confusion Matrix Drift</td>\n",
              "      <td id=\"T_87ae0_row161_col2\" class=\"data row161 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row161_col3\" class=\"data row161 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row161_col4\" class=\"data row161 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row161_col5\" class=\"data row161 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_87ae0_row161_col6\" class=\"data row161 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row162_col0\" class=\"data row162 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
              "      <td id=\"T_87ae0_row162_col1\" class=\"data row162 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
              "      <td id=\"T_87ae0_row162_col2\" class=\"data row162 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row162_col3\" class=\"data row162 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row162_col4\" class=\"data row162 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row162_col5\" class=\"data row162 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_87ae0_row162_col6\" class=\"data row162 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row163_col0\" class=\"data row163 col0\" >validmind.ongoing_monitoring.FeatureDrift</td>\n",
              "      <td id=\"T_87ae0_row163_col1\" class=\"data row163 col1\" >Feature Drift</td>\n",
              "      <td id=\"T_87ae0_row163_col2\" class=\"data row163 col2\" >Evaluates changes in feature distribution over time to identify potential model drift....</td>\n",
              "      <td id=\"T_87ae0_row163_col3\" class=\"data row163 col3\" >['datasets']</td>\n",
              "      <td id=\"T_87ae0_row163_col4\" class=\"data row163 col4\" >{'bins': {'type': '_empty', 'default': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, 'feature_columns': {'type': '_empty', 'default': None}, 'psi_threshold': {'type': '_empty', 'default': 0.2}}</td>\n",
              "      <td id=\"T_87ae0_row163_col5\" class=\"data row163 col5\" >['visualization']</td>\n",
              "      <td id=\"T_87ae0_row163_col6\" class=\"data row163 col6\" >['monitoring']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row164_col0\" class=\"data row164 col0\" >validmind.ongoing_monitoring.PredictionAcrossEachFeature</td>\n",
              "      <td id=\"T_87ae0_row164_col1\" class=\"data row164 col1\" >Prediction Across Each Feature</td>\n",
              "      <td id=\"T_87ae0_row164_col2\" class=\"data row164 col2\" >Assesses differences in model predictions across individual features between reference and monitoring datasets...</td>\n",
              "      <td id=\"T_87ae0_row164_col3\" class=\"data row164 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row164_col4\" class=\"data row164 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row164_col5\" class=\"data row164 col5\" >['visualization']</td>\n",
              "      <td id=\"T_87ae0_row164_col6\" class=\"data row164 col6\" >['monitoring']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row165_col0\" class=\"data row165 col0\" >validmind.ongoing_monitoring.PredictionCorrelation</td>\n",
              "      <td id=\"T_87ae0_row165_col1\" class=\"data row165 col1\" >Prediction Correlation</td>\n",
              "      <td id=\"T_87ae0_row165_col2\" class=\"data row165 col2\" >Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential...</td>\n",
              "      <td id=\"T_87ae0_row165_col3\" class=\"data row165 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row165_col4\" class=\"data row165 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row165_col5\" class=\"data row165 col5\" >['visualization']</td>\n",
              "      <td id=\"T_87ae0_row165_col6\" class=\"data row165 col6\" >['monitoring']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row166_col0\" class=\"data row166 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
              "      <td id=\"T_87ae0_row166_col1\" class=\"data row166 col1\" >Prediction Probabilities Histogram Drift</td>\n",
              "      <td id=\"T_87ae0_row166_col2\" class=\"data row166 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row166_col3\" class=\"data row166 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row166_col4\" class=\"data row166 col4\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_87ae0_row166_col5\" class=\"data row166 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_87ae0_row166_col6\" class=\"data row166 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row167_col0\" class=\"data row167 col0\" >validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures</td>\n",
              "      <td id=\"T_87ae0_row167_col1\" class=\"data row167 col1\" >Prediction Quantiles Across Features</td>\n",
              "      <td id=\"T_87ae0_row167_col2\" class=\"data row167 col2\" >Assesses differences in model prediction distributions across individual features between reference...</td>\n",
              "      <td id=\"T_87ae0_row167_col3\" class=\"data row167 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row167_col4\" class=\"data row167 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row167_col5\" class=\"data row167 col5\" >['visualization']</td>\n",
              "      <td id=\"T_87ae0_row167_col6\" class=\"data row167 col6\" >['monitoring']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row168_col0\" class=\"data row168 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
              "      <td id=\"T_87ae0_row168_col1\" class=\"data row168 col1\" >ROC Curve Drift</td>\n",
              "      <td id=\"T_87ae0_row168_col2\" class=\"data row168 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_87ae0_row168_col3\" class=\"data row168 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row168_col4\" class=\"data row168 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row168_col5\" class=\"data row168 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_87ae0_row168_col6\" class=\"data row168 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row169_col0\" class=\"data row169 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
              "      <td id=\"T_87ae0_row169_col1\" class=\"data row169 col1\" >Score Bands Drift</td>\n",
              "      <td id=\"T_87ae0_row169_col2\" class=\"data row169 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
              "      <td id=\"T_87ae0_row169_col3\" class=\"data row169 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row169_col4\" class=\"data row169 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_87ae0_row169_col5\" class=\"data row169 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
              "      <td id=\"T_87ae0_row169_col6\" class=\"data row169 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row170_col0\" class=\"data row170 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
              "      <td id=\"T_87ae0_row170_col1\" class=\"data row170 col1\" >Scorecard Histogram Drift</td>\n",
              "      <td id=\"T_87ae0_row170_col2\" class=\"data row170 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
              "      <td id=\"T_87ae0_row170_col3\" class=\"data row170 col3\" >['datasets']</td>\n",
              "      <td id=\"T_87ae0_row170_col4\" class=\"data row170 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_87ae0_row170_col5\" class=\"data row170 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
              "      <td id=\"T_87ae0_row170_col6\" class=\"data row170 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row171_col0\" class=\"data row171 col0\" >validmind.ongoing_monitoring.TargetPredictionDistributionPlot</td>\n",
              "      <td id=\"T_87ae0_row171_col1\" class=\"data row171 col1\" >Target Prediction Distribution Plot</td>\n",
              "      <td id=\"T_87ae0_row171_col2\" class=\"data row171 col2\" >Assesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify...</td>\n",
              "      <td id=\"T_87ae0_row171_col3\" class=\"data row171 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_87ae0_row171_col4\" class=\"data row171 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_87ae0_row171_col5\" class=\"data row171 col5\" >['visualization']</td>\n",
              "      <td id=\"T_87ae0_row171_col6\" class=\"data row171 col6\" >['monitoring']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row172_col0\" class=\"data row172 col0\" >validmind.prompt_validation.Bias</td>\n",
              "      <td id=\"T_87ae0_row172_col1\" class=\"data row172 col1\" >Bias</td>\n",
              "      <td id=\"T_87ae0_row172_col2\" class=\"data row172 col2\" >Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...</td>\n",
              "      <td id=\"T_87ae0_row172_col3\" class=\"data row172 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row172_col4\" class=\"data row172 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row172_col5\" class=\"data row172 col5\" >['llm', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row172_col6\" class=\"data row172 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row173_col0\" class=\"data row173 col0\" >validmind.prompt_validation.Clarity</td>\n",
              "      <td id=\"T_87ae0_row173_col1\" class=\"data row173 col1\" >Clarity</td>\n",
              "      <td id=\"T_87ae0_row173_col2\" class=\"data row173 col2\" >Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....</td>\n",
              "      <td id=\"T_87ae0_row173_col3\" class=\"data row173 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row173_col4\" class=\"data row173 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row173_col5\" class=\"data row173 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row173_col6\" class=\"data row173 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row174_col0\" class=\"data row174 col0\" >validmind.prompt_validation.Conciseness</td>\n",
              "      <td id=\"T_87ae0_row174_col1\" class=\"data row174 col1\" >Conciseness</td>\n",
              "      <td id=\"T_87ae0_row174_col2\" class=\"data row174 col2\" >Analyzes and grades the conciseness of prompts provided to a Large Language Model....</td>\n",
              "      <td id=\"T_87ae0_row174_col3\" class=\"data row174 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row174_col4\" class=\"data row174 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row174_col5\" class=\"data row174 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row174_col6\" class=\"data row174 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row175_col0\" class=\"data row175 col0\" >validmind.prompt_validation.Delimitation</td>\n",
              "      <td id=\"T_87ae0_row175_col1\" class=\"data row175 col1\" >Delimitation</td>\n",
              "      <td id=\"T_87ae0_row175_col2\" class=\"data row175 col2\" >Evaluates the proper use of delimiters in prompts provided to Large Language Models....</td>\n",
              "      <td id=\"T_87ae0_row175_col3\" class=\"data row175 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row175_col4\" class=\"data row175 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row175_col5\" class=\"data row175 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row175_col6\" class=\"data row175 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row176_col0\" class=\"data row176 col0\" >validmind.prompt_validation.NegativeInstruction</td>\n",
              "      <td id=\"T_87ae0_row176_col1\" class=\"data row176 col1\" >Negative Instruction</td>\n",
              "      <td id=\"T_87ae0_row176_col2\" class=\"data row176 col2\" >Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....</td>\n",
              "      <td id=\"T_87ae0_row176_col3\" class=\"data row176 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row176_col4\" class=\"data row176 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row176_col5\" class=\"data row176 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row176_col6\" class=\"data row176 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row177_col0\" class=\"data row177 col0\" >validmind.prompt_validation.Robustness</td>\n",
              "      <td id=\"T_87ae0_row177_col1\" class=\"data row177 col1\" >Robustness</td>\n",
              "      <td id=\"T_87ae0_row177_col2\" class=\"data row177 col2\" >Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test...</td>\n",
              "      <td id=\"T_87ae0_row177_col3\" class=\"data row177 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row177_col4\" class=\"data row177 col4\" >{'num_tests': {'type': '_empty', 'default': 10}}</td>\n",
              "      <td id=\"T_87ae0_row177_col5\" class=\"data row177 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row177_col6\" class=\"data row177 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row178_col0\" class=\"data row178 col0\" >validmind.prompt_validation.Specificity</td>\n",
              "      <td id=\"T_87ae0_row178_col1\" class=\"data row178 col1\" >Specificity</td>\n",
              "      <td id=\"T_87ae0_row178_col2\" class=\"data row178 col2\" >Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,...</td>\n",
              "      <td id=\"T_87ae0_row178_col3\" class=\"data row178 col3\" >['model']</td>\n",
              "      <td id=\"T_87ae0_row178_col4\" class=\"data row178 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
              "      <td id=\"T_87ae0_row178_col5\" class=\"data row178 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
              "      <td id=\"T_87ae0_row178_col6\" class=\"data row178 col6\" >['text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row179_col0\" class=\"data row179 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
              "      <td id=\"T_87ae0_row179_col1\" class=\"data row179 col1\" >Accuracy</td>\n",
              "      <td id=\"T_87ae0_row179_col2\" class=\"data row179 col2\" >Calculates the accuracy of a model</td>\n",
              "      <td id=\"T_87ae0_row179_col3\" class=\"data row179 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row179_col4\" class=\"data row179 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row179_col5\" class=\"data row179 col5\" >['classification']</td>\n",
              "      <td id=\"T_87ae0_row179_col6\" class=\"data row179 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row180_col0\" class=\"data row180 col0\" >validmind.unit_metrics.classification.F1</td>\n",
              "      <td id=\"T_87ae0_row180_col1\" class=\"data row180 col1\" >F1</td>\n",
              "      <td id=\"T_87ae0_row180_col2\" class=\"data row180 col2\" >Calculates the F1 score for a classification model.</td>\n",
              "      <td id=\"T_87ae0_row180_col3\" class=\"data row180 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row180_col4\" class=\"data row180 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row180_col5\" class=\"data row180 col5\" >['classification']</td>\n",
              "      <td id=\"T_87ae0_row180_col6\" class=\"data row180 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row181_col0\" class=\"data row181 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
              "      <td id=\"T_87ae0_row181_col1\" class=\"data row181 col1\" >Precision</td>\n",
              "      <td id=\"T_87ae0_row181_col2\" class=\"data row181 col2\" >Calculates the precision for a classification model.</td>\n",
              "      <td id=\"T_87ae0_row181_col3\" class=\"data row181 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row181_col4\" class=\"data row181 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row181_col5\" class=\"data row181 col5\" >['classification']</td>\n",
              "      <td id=\"T_87ae0_row181_col6\" class=\"data row181 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row182_col0\" class=\"data row182 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
              "      <td id=\"T_87ae0_row182_col1\" class=\"data row182 col1\" >ROC AUC</td>\n",
              "      <td id=\"T_87ae0_row182_col2\" class=\"data row182 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
              "      <td id=\"T_87ae0_row182_col3\" class=\"data row182 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row182_col4\" class=\"data row182 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row182_col5\" class=\"data row182 col5\" >['classification']</td>\n",
              "      <td id=\"T_87ae0_row182_col6\" class=\"data row182 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row183_col0\" class=\"data row183 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
              "      <td id=\"T_87ae0_row183_col1\" class=\"data row183 col1\" >Recall</td>\n",
              "      <td id=\"T_87ae0_row183_col2\" class=\"data row183 col2\" >Calculates the recall for a classification model.</td>\n",
              "      <td id=\"T_87ae0_row183_col3\" class=\"data row183 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row183_col4\" class=\"data row183 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row183_col5\" class=\"data row183 col5\" >['classification']</td>\n",
              "      <td id=\"T_87ae0_row183_col6\" class=\"data row183 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row184_col0\" class=\"data row184 col0\" >validmind.unit_metrics.regression.AdjustedRSquaredScore</td>\n",
              "      <td id=\"T_87ae0_row184_col1\" class=\"data row184 col1\" >Adjusted R Squared Score</td>\n",
              "      <td id=\"T_87ae0_row184_col2\" class=\"data row184 col2\" >Calculates the adjusted R-squared score for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row184_col3\" class=\"data row184 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row184_col4\" class=\"data row184 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row184_col5\" class=\"data row184 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row184_col6\" class=\"data row184 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row185_col0\" class=\"data row185 col0\" >validmind.unit_metrics.regression.GiniCoefficient</td>\n",
              "      <td id=\"T_87ae0_row185_col1\" class=\"data row185 col1\" >Gini Coefficient</td>\n",
              "      <td id=\"T_87ae0_row185_col2\" class=\"data row185 col2\" >Calculates the Gini coefficient for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row185_col3\" class=\"data row185 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row185_col4\" class=\"data row185 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row185_col5\" class=\"data row185 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row185_col6\" class=\"data row185 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row186_col0\" class=\"data row186 col0\" >validmind.unit_metrics.regression.HuberLoss</td>\n",
              "      <td id=\"T_87ae0_row186_col1\" class=\"data row186 col1\" >Huber Loss</td>\n",
              "      <td id=\"T_87ae0_row186_col2\" class=\"data row186 col2\" >Calculates the Huber loss for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row186_col3\" class=\"data row186 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row186_col4\" class=\"data row186 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row186_col5\" class=\"data row186 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row186_col6\" class=\"data row186 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row187_col0\" class=\"data row187 col0\" >validmind.unit_metrics.regression.KolmogorovSmirnovStatistic</td>\n",
              "      <td id=\"T_87ae0_row187_col1\" class=\"data row187 col1\" >Kolmogorov Smirnov Statistic</td>\n",
              "      <td id=\"T_87ae0_row187_col2\" class=\"data row187 col2\" >Calculates the Kolmogorov-Smirnov statistic for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row187_col3\" class=\"data row187 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_87ae0_row187_col4\" class=\"data row187 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row187_col5\" class=\"data row187 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row187_col6\" class=\"data row187 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row188_col0\" class=\"data row188 col0\" >validmind.unit_metrics.regression.MeanAbsoluteError</td>\n",
              "      <td id=\"T_87ae0_row188_col1\" class=\"data row188 col1\" >Mean Absolute Error</td>\n",
              "      <td id=\"T_87ae0_row188_col2\" class=\"data row188 col2\" >Calculates the mean absolute error for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row188_col3\" class=\"data row188 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row188_col4\" class=\"data row188 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row188_col5\" class=\"data row188 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row188_col6\" class=\"data row188 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row189_col0\" class=\"data row189 col0\" >validmind.unit_metrics.regression.MeanAbsolutePercentageError</td>\n",
              "      <td id=\"T_87ae0_row189_col1\" class=\"data row189 col1\" >Mean Absolute Percentage Error</td>\n",
              "      <td id=\"T_87ae0_row189_col2\" class=\"data row189 col2\" >Calculates the mean absolute percentage error for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row189_col3\" class=\"data row189 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row189_col4\" class=\"data row189 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row189_col5\" class=\"data row189 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row189_col6\" class=\"data row189 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row190_col0\" class=\"data row190 col0\" >validmind.unit_metrics.regression.MeanBiasDeviation</td>\n",
              "      <td id=\"T_87ae0_row190_col1\" class=\"data row190 col1\" >Mean Bias Deviation</td>\n",
              "      <td id=\"T_87ae0_row190_col2\" class=\"data row190 col2\" >Calculates the mean bias deviation for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row190_col3\" class=\"data row190 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row190_col4\" class=\"data row190 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row190_col5\" class=\"data row190 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row190_col6\" class=\"data row190 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row191_col0\" class=\"data row191 col0\" >validmind.unit_metrics.regression.MeanSquaredError</td>\n",
              "      <td id=\"T_87ae0_row191_col1\" class=\"data row191 col1\" >Mean Squared Error</td>\n",
              "      <td id=\"T_87ae0_row191_col2\" class=\"data row191 col2\" >Calculates the mean squared error for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row191_col3\" class=\"data row191 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row191_col4\" class=\"data row191 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row191_col5\" class=\"data row191 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row191_col6\" class=\"data row191 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row192_col0\" class=\"data row192 col0\" >validmind.unit_metrics.regression.QuantileLoss</td>\n",
              "      <td id=\"T_87ae0_row192_col1\" class=\"data row192 col1\" >Quantile Loss</td>\n",
              "      <td id=\"T_87ae0_row192_col2\" class=\"data row192 col2\" >Calculates the quantile loss for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row192_col3\" class=\"data row192 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row192_col4\" class=\"data row192 col4\" >{'quantile': {'type': '_empty', 'default': 0.5}}</td>\n",
              "      <td id=\"T_87ae0_row192_col5\" class=\"data row192 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row192_col6\" class=\"data row192 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row193_col0\" class=\"data row193 col0\" >validmind.unit_metrics.regression.RSquaredScore</td>\n",
              "      <td id=\"T_87ae0_row193_col1\" class=\"data row193 col1\" >R Squared Score</td>\n",
              "      <td id=\"T_87ae0_row193_col2\" class=\"data row193 col2\" >Calculates the R-squared score for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row193_col3\" class=\"data row193 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row193_col4\" class=\"data row193 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row193_col5\" class=\"data row193 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row193_col6\" class=\"data row193 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_87ae0_row194_col0\" class=\"data row194 col0\" >validmind.unit_metrics.regression.RootMeanSquaredError</td>\n",
              "      <td id=\"T_87ae0_row194_col1\" class=\"data row194 col1\" >Root Mean Squared Error</td>\n",
              "      <td id=\"T_87ae0_row194_col2\" class=\"data row194 col2\" >Calculates the root mean squared error for a regression model.</td>\n",
              "      <td id=\"T_87ae0_row194_col3\" class=\"data row194 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_87ae0_row194_col4\" class=\"data row194 col4\" >{}</td>\n",
              "      <td id=\"T_87ae0_row194_col5\" class=\"data row194 col5\" >['regression']</td>\n",
              "      <td id=\"T_87ae0_row194_col6\" class=\"data row194 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x37556ee20>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tests()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id='toc2_'></a>\n",
        "\n",
        "## Understanding Tags and Task Types\n",
        "\n",
        "Effectively using ValidMind's tests involves a deep understanding of its 'tags' and 'task types'. Here's a breakdown:\n",
        "\n",
        "- **Task Types**: Represent the kind of modeling task associated with a test. For instance:\n",
        "\n",
        "  - **classification:** Works with Classification Models and Datasets\n",
        "  - **regression:** Works with Regression Models and Datasets\n",
        "  - **text classification:** Works with Text Classification Models and Datasets\n",
        "  - **text summarization:** Works with Text Summarization Models and Datasets\n",
        "\n",
        "- **Tags**: Free-form descriptors providing more details about the test, what data and models the test is compatible with and what category the test falls into etc. Some examples include:\n",
        "  - **llm:** Tests that work with Large Language Models\n",
        "  - **nlp:** Tests relevant for natural language processing.\n",
        "  - **binary_classification:** Tests for binary classification tasks.\n",
        "  - **forecasting:** Tests for forecasting and time-series analysis.\n",
        "  - **tabular_data:** Tests for tabular data like CSVs and Excel spreadsheets.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can use the functions list_tasks() and list_tags() to view all the tasks and tags used for classifying all the tests available in the ValidMind Library:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['time_series_forecasting',\n",
              " 'feature_extraction',\n",
              " 'text_qa',\n",
              " 'text_generation',\n",
              " 'residual_analysis',\n",
              " 'visualization',\n",
              " 'text_classification',\n",
              " 'regression',\n",
              " 'nlp',\n",
              " 'text_summarization',\n",
              " 'data_validation',\n",
              " 'classification',\n",
              " 'clustering',\n",
              " 'monitoring']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tasks()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['few_shot',\n",
              " 'ragas',\n",
              " 'bias_and_fairness',\n",
              " 'AUC',\n",
              " 'visualization',\n",
              " 'rag_performance',\n",
              " 'logistic_regression',\n",
              " 'model_validation',\n",
              " 'credit_risk',\n",
              " 'model_selection',\n",
              " 'linear_regression',\n",
              " 'clustering',\n",
              " 'data_distribution',\n",
              " 'model_explainability',\n",
              " 'frequency_analysis',\n",
              " 'model_interpretation',\n",
              " 'time_series_data',\n",
              " 'forecasting',\n",
              " 'llm',\n",
              " 'multiclass_classification',\n",
              " 'data_validation',\n",
              " 'binary_classification',\n",
              " 'stationarity',\n",
              " 'senstivity_analysis',\n",
              " 'retrieval_performance',\n",
              " 'categorical_data',\n",
              " 'seasonality',\n",
              " 'qualitative',\n",
              " 'model_comparison',\n",
              " 'model_training',\n",
              " 'data_quality',\n",
              " 'regression',\n",
              " 'anomaly_detection',\n",
              " 'calibration',\n",
              " 'model_predictions',\n",
              " 'dimensionality_reduction',\n",
              " 'descriptive_statistics',\n",
              " 'classification',\n",
              " 'unit_root_test',\n",
              " 'metadata',\n",
              " 'threshold_optimization',\n",
              " 'model_diagnosis',\n",
              " 'feature_selection',\n",
              " 'data_analysis',\n",
              " 'statistical_test',\n",
              " 'embeddings',\n",
              " 'analysis',\n",
              " 'feature_importance',\n",
              " 'scorecard',\n",
              " 'correlation',\n",
              " 'classification_metrics',\n",
              " 'nlp',\n",
              " 'sklearn',\n",
              " 'kmeans',\n",
              " 'statsmodels',\n",
              " 'numerical_data',\n",
              " 'zero_shot',\n",
              " 'text_data',\n",
              " 'tabular_data',\n",
              " 'model_performance']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tags()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you want to see which tags correspond to which task type, you can use the function `list_tasks_and_tags()`:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "#T_fae72_row0_col0, #T_fae72_row0_col1, #T_fae72_row1_col0, #T_fae72_row1_col1, #T_fae72_row2_col0, #T_fae72_row2_col1, #T_fae72_row3_col0, #T_fae72_row3_col1, #T_fae72_row4_col0, #T_fae72_row4_col1, #T_fae72_row5_col0, #T_fae72_row5_col1, #T_fae72_row6_col0, #T_fae72_row6_col1, #T_fae72_row7_col0, #T_fae72_row7_col1, #T_fae72_row8_col0, #T_fae72_row8_col1, #T_fae72_row9_col0, #T_fae72_row9_col1, #T_fae72_row10_col0, #T_fae72_row10_col1, #T_fae72_row11_col0, #T_fae72_row11_col1, #T_fae72_row12_col0, #T_fae72_row12_col1, #T_fae72_row13_col0, #T_fae72_row13_col1 {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_fae72\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_fae72_level0_col0\" class=\"col_heading level0 col0\" >Task</th>\n",
              "      <th id=\"T_fae72_level0_col1\" class=\"col_heading level0 col1\" >Tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row0_col0\" class=\"data row0 col0\" >regression</td>\n",
              "      <td id=\"T_fae72_row0_col1\" class=\"data row0 col1\" >bias_and_fairness, visualization, model_selection, linear_regression, data_distribution, model_explainability, model_interpretation, time_series_data, forecasting, multiclass_classification, data_validation, binary_classification, stationarity, model_performance, senstivity_analysis, categorical_data, seasonality, data_quality, regression, model_predictions, descriptive_statistics, unit_root_test, metadata, model_diagnosis, feature_selection, data_analysis, statistical_test, analysis, feature_importance, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row1_col0\" class=\"data row1 col0\" >classification</td>\n",
              "      <td id=\"T_fae72_row1_col1\" class=\"data row1 col1\" >bias_and_fairness, AUC, visualization, logistic_regression, model_validation, credit_risk, linear_regression, data_distribution, time_series_data, multiclass_classification, binary_classification, categorical_data, model_comparison, model_training, data_quality, anomaly_detection, calibration, descriptive_statistics, classification, metadata, model_diagnosis, threshold_optimization, feature_selection, data_analysis, statistical_test, classification_metrics, feature_importance, scorecard, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_performance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row2_col0\" class=\"data row2 col0\" >text_classification</td>\n",
              "      <td id=\"T_fae72_row2_col1\" class=\"data row2 col1\" >few_shot, ragas, visualization, frequency_analysis, model_comparison, feature_importance, time_series_data, nlp, llm, sklearn, multiclass_classification, zero_shot, text_data, binary_classification, retrieval_performance, tabular_data, model_performance, model_diagnosis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row3_col0\" class=\"data row3 col0\" >text_summarization</td>\n",
              "      <td id=\"T_fae72_row3_col1\" class=\"data row3 col1\" >few_shot, ragas, qualitative, visualization, frequency_analysis, embeddings, rag_performance, time_series_data, nlp, llm, zero_shot, text_data, dimensionality_reduction, retrieval_performance, tabular_data</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row4_col0\" class=\"data row4 col0\" >data_validation</td>\n",
              "      <td id=\"T_fae72_row4_col1\" class=\"data row4 col1\" >stationarity, time_series_data, statsmodels, unit_root_test</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row5_col0\" class=\"data row5 col0\" >time_series_forecasting</td>\n",
              "      <td id=\"T_fae72_row5_col1\" class=\"data row5 col1\" >model_explainability, visualization, time_series_data, sklearn, model_predictions, data_validation, model_performance, model_training, metadata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row6_col0\" class=\"data row6 col0\" >nlp</td>\n",
              "      <td id=\"T_fae72_row6_col1\" class=\"data row6 col1\" >visualization, frequency_analysis, data_validation, nlp, text_data</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row7_col0\" class=\"data row7 col0\" >clustering</td>\n",
              "      <td id=\"T_fae72_row7_col1\" class=\"data row7 col1\" >sklearn, kmeans, clustering, model_performance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row8_col0\" class=\"data row8 col0\" >residual_analysis</td>\n",
              "      <td id=\"T_fae72_row8_col1\" class=\"data row8 col1\" >regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row9_col0\" class=\"data row9 col0\" >visualization</td>\n",
              "      <td id=\"T_fae72_row9_col1\" class=\"data row9 col1\" >regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row10_col0\" class=\"data row10 col0\" >feature_extraction</td>\n",
              "      <td id=\"T_fae72_row10_col1\" class=\"data row10 col1\" >text_data, llm, visualization, embeddings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row11_col0\" class=\"data row11 col0\" >text_qa</td>\n",
              "      <td id=\"T_fae72_row11_col1\" class=\"data row11 col1\" >ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row12_col0\" class=\"data row12 col0\" >text_generation</td>\n",
              "      <td id=\"T_fae72_row12_col1\" class=\"data row12 col1\" >ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_fae72_row13_col0\" class=\"data row13 col0\" >monitoring</td>\n",
              "      <td id=\"T_fae72_row13_col1\" class=\"data row13 col1\" >visualization</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x3618a93a0>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tasks_and_tags()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id='toc3_'></a>\n",
        "\n",
        "## Searching for Specific Tests using `tags` and `tasks`\n",
        "\n",
        "While listing all tests is valuable, there are times when you need to narrow down your search. The `list_tests` function offers `filter`, `task`, and `tags` parameters to assist in this.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you're targeting a specific test or tests that match a particular task type, the `filter` parameter comes in handy. For example, to list tests that are compatible with 'sklearn' models:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_05adb th {\n",
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#T_05adb_row40_col5, #T_05adb_row40_col6, #T_05adb_row41_col0, #T_05adb_row41_col1, #T_05adb_row41_col2, #T_05adb_row41_col3, #T_05adb_row41_col4, #T_05adb_row41_col5, #T_05adb_row41_col6, #T_05adb_row42_col0, #T_05adb_row42_col1, #T_05adb_row42_col2, #T_05adb_row42_col3, #T_05adb_row42_col4, #T_05adb_row42_col5, #T_05adb_row42_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_05adb\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_05adb_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
              "      <th id=\"T_05adb_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
              "      <th id=\"T_05adb_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
              "      <th id=\"T_05adb_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
              "      <th id=\"T_05adb_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
              "      <th id=\"T_05adb_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
              "      <th id=\"T_05adb_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
              "      <td id=\"T_05adb_row0_col1\" class=\"data row0 col1\" >Cluster Size Distribution</td>\n",
              "      <td id=\"T_05adb_row0_col2\" class=\"data row0 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
              "      <td id=\"T_05adb_row0_col3\" class=\"data row0 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row0_col5\" class=\"data row0 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row0_col6\" class=\"data row0 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
              "      <td id=\"T_05adb_row1_col1\" class=\"data row1 col1\" >Time Series R2 Square By Segments</td>\n",
              "      <td id=\"T_05adb_row1_col2\" class=\"data row1 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
              "      <td id=\"T_05adb_row1_col3\" class=\"data row1 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row1_col4\" class=\"data row1 col4\" >{'segments': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row1_col5\" class=\"data row1 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_05adb_row1_col6\" class=\"data row1 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
              "      <td id=\"T_05adb_row2_col1\" class=\"data row2 col1\" >Adjusted Mutual Information</td>\n",
              "      <td id=\"T_05adb_row2_col2\" class=\"data row2 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
              "      <td id=\"T_05adb_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row2_col5\" class=\"data row2 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_05adb_row2_col6\" class=\"data row2 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
              "      <td id=\"T_05adb_row3_col1\" class=\"data row3 col1\" >Adjusted Rand Index</td>\n",
              "      <td id=\"T_05adb_row3_col2\" class=\"data row3 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
              "      <td id=\"T_05adb_row3_col3\" class=\"data row3 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row3_col5\" class=\"data row3 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_05adb_row3_col6\" class=\"data row3 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
              "      <td id=\"T_05adb_row4_col1\" class=\"data row4 col1\" >Calibration Curve</td>\n",
              "      <td id=\"T_05adb_row4_col2\" class=\"data row4 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
              "      <td id=\"T_05adb_row4_col3\" class=\"data row4 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row4_col4\" class=\"data row4 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_05adb_row4_col5\" class=\"data row4 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
              "      <td id=\"T_05adb_row4_col6\" class=\"data row4 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row5_col0\" class=\"data row5 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
              "      <td id=\"T_05adb_row5_col1\" class=\"data row5 col1\" >Classifier Performance</td>\n",
              "      <td id=\"T_05adb_row5_col2\" class=\"data row5 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
              "      <td id=\"T_05adb_row5_col3\" class=\"data row5 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row5_col4\" class=\"data row5 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
              "      <td id=\"T_05adb_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row6_col0\" class=\"data row6 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
              "      <td id=\"T_05adb_row6_col1\" class=\"data row6 col1\" >Classifier Threshold Optimization</td>\n",
              "      <td id=\"T_05adb_row6_col2\" class=\"data row6 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
              "      <td id=\"T_05adb_row6_col3\" class=\"data row6 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row6_col4\" class=\"data row6 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row6_col5\" class=\"data row6 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
              "      <td id=\"T_05adb_row6_col6\" class=\"data row6 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
              "      <td id=\"T_05adb_row7_col1\" class=\"data row7 col1\" >Cluster Cosine Similarity</td>\n",
              "      <td id=\"T_05adb_row7_col2\" class=\"data row7 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
              "      <td id=\"T_05adb_row7_col3\" class=\"data row7 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row7_col4\" class=\"data row7 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row7_col5\" class=\"data row7 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_05adb_row7_col6\" class=\"data row7 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
              "      <td id=\"T_05adb_row8_col1\" class=\"data row8 col1\" >Cluster Performance Metrics</td>\n",
              "      <td id=\"T_05adb_row8_col2\" class=\"data row8 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
              "      <td id=\"T_05adb_row8_col3\" class=\"data row8 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row8_col4\" class=\"data row8 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row8_col5\" class=\"data row8 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_05adb_row8_col6\" class=\"data row8 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
              "      <td id=\"T_05adb_row9_col1\" class=\"data row9 col1\" >Completeness Score</td>\n",
              "      <td id=\"T_05adb_row9_col2\" class=\"data row9 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
              "      <td id=\"T_05adb_row9_col3\" class=\"data row9 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row9_col5\" class=\"data row9 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
              "      <td id=\"T_05adb_row9_col6\" class=\"data row9 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
              "      <td id=\"T_05adb_row10_col1\" class=\"data row10 col1\" >Confusion Matrix</td>\n",
              "      <td id=\"T_05adb_row10_col2\" class=\"data row10 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
              "      <td id=\"T_05adb_row10_col3\" class=\"data row10 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row10_col4\" class=\"data row10 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_05adb_row10_col5\" class=\"data row10 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row10_col6\" class=\"data row10 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
              "      <td id=\"T_05adb_row11_col1\" class=\"data row11 col1\" >Feature Importance</td>\n",
              "      <td id=\"T_05adb_row11_col2\" class=\"data row11 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
              "      <td id=\"T_05adb_row11_col3\" class=\"data row11 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row11_col4\" class=\"data row11 col4\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
              "      <td id=\"T_05adb_row11_col5\" class=\"data row11 col5\" >['model_explainability', 'sklearn']</td>\n",
              "      <td id=\"T_05adb_row11_col6\" class=\"data row11 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
              "      <td id=\"T_05adb_row12_col1\" class=\"data row12 col1\" >Fowlkes Mallows Score</td>\n",
              "      <td id=\"T_05adb_row12_col2\" class=\"data row12 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
              "      <td id=\"T_05adb_row12_col3\" class=\"data row12 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row12_col4\" class=\"data row12 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row12_col5\" class=\"data row12 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row12_col6\" class=\"data row12 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
              "      <td id=\"T_05adb_row13_col1\" class=\"data row13 col1\" >Homogeneity Score</td>\n",
              "      <td id=\"T_05adb_row13_col2\" class=\"data row13 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
              "      <td id=\"T_05adb_row13_col3\" class=\"data row13 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row13_col4\" class=\"data row13 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row13_col5\" class=\"data row13 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row13_col6\" class=\"data row13 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
              "      <td id=\"T_05adb_row14_col1\" class=\"data row14 col1\" >Hyper Parameters Tuning</td>\n",
              "      <td id=\"T_05adb_row14_col2\" class=\"data row14 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
              "      <td id=\"T_05adb_row14_col3\" class=\"data row14 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row14_col4\" class=\"data row14 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row14_col5\" class=\"data row14 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row14_col6\" class=\"data row14 col6\" >['clustering', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
              "      <td id=\"T_05adb_row15_col1\" class=\"data row15 col1\" >K Means Clusters Optimization</td>\n",
              "      <td id=\"T_05adb_row15_col2\" class=\"data row15 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
              "      <td id=\"T_05adb_row15_col3\" class=\"data row15 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row15_col4\" class=\"data row15 col4\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row15_col5\" class=\"data row15 col5\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
              "      <td id=\"T_05adb_row15_col6\" class=\"data row15 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
              "      <td id=\"T_05adb_row16_col1\" class=\"data row16 col1\" >Minimum Accuracy</td>\n",
              "      <td id=\"T_05adb_row16_col2\" class=\"data row16 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_05adb_row16_col3\" class=\"data row16 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row16_col4\" class=\"data row16 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_05adb_row16_col5\" class=\"data row16 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row16_col6\" class=\"data row16 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
              "      <td id=\"T_05adb_row17_col1\" class=\"data row17 col1\" >Minimum F1 Score</td>\n",
              "      <td id=\"T_05adb_row17_col2\" class=\"data row17 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
              "      <td id=\"T_05adb_row17_col3\" class=\"data row17 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row17_col4\" class=\"data row17 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_05adb_row17_col5\" class=\"data row17 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row17_col6\" class=\"data row17 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
              "      <td id=\"T_05adb_row18_col1\" class=\"data row18 col1\" >Minimum ROCAUC Score</td>\n",
              "      <td id=\"T_05adb_row18_col2\" class=\"data row18 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_05adb_row18_col3\" class=\"data row18 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row18_col4\" class=\"data row18 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_05adb_row18_col5\" class=\"data row18 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row18_col6\" class=\"data row18 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
              "      <td id=\"T_05adb_row19_col1\" class=\"data row19 col1\" >Model Parameters</td>\n",
              "      <td id=\"T_05adb_row19_col2\" class=\"data row19 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
              "      <td id=\"T_05adb_row19_col3\" class=\"data row19 col3\" >['model']</td>\n",
              "      <td id=\"T_05adb_row19_col4\" class=\"data row19 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row19_col5\" class=\"data row19 col5\" >['model_training', 'metadata']</td>\n",
              "      <td id=\"T_05adb_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
              "      <td id=\"T_05adb_row20_col1\" class=\"data row20 col1\" >Models Performance Comparison</td>\n",
              "      <td id=\"T_05adb_row20_col2\" class=\"data row20 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
              "      <td id=\"T_05adb_row20_col3\" class=\"data row20 col3\" >['dataset', 'models']</td>\n",
              "      <td id=\"T_05adb_row20_col4\" class=\"data row20 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row20_col5\" class=\"data row20 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
              "      <td id=\"T_05adb_row20_col6\" class=\"data row20 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
              "      <td id=\"T_05adb_row21_col1\" class=\"data row21 col1\" >Overfit Diagnosis</td>\n",
              "      <td id=\"T_05adb_row21_col2\" class=\"data row21 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
              "      <td id=\"T_05adb_row21_col3\" class=\"data row21 col3\" >['model', 'datasets']</td>\n",
              "      <td id=\"T_05adb_row21_col4\" class=\"data row21 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
              "      <td id=\"T_05adb_row21_col5\" class=\"data row21 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
              "      <td id=\"T_05adb_row21_col6\" class=\"data row21 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
              "      <td id=\"T_05adb_row22_col1\" class=\"data row22 col1\" >Permutation Feature Importance</td>\n",
              "      <td id=\"T_05adb_row22_col2\" class=\"data row22 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
              "      <td id=\"T_05adb_row22_col3\" class=\"data row22 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row22_col4\" class=\"data row22 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row22_col5\" class=\"data row22 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row22_col6\" class=\"data row22 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
              "      <td id=\"T_05adb_row23_col1\" class=\"data row23 col1\" >Population Stability Index</td>\n",
              "      <td id=\"T_05adb_row23_col2\" class=\"data row23 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
              "      <td id=\"T_05adb_row23_col3\" class=\"data row23 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row23_col4\" class=\"data row23 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
              "      <td id=\"T_05adb_row23_col5\" class=\"data row23 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row23_col6\" class=\"data row23 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
              "      <td id=\"T_05adb_row24_col1\" class=\"data row24 col1\" >Precision Recall Curve</td>\n",
              "      <td id=\"T_05adb_row24_col2\" class=\"data row24 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
              "      <td id=\"T_05adb_row24_col3\" class=\"data row24 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row24_col4\" class=\"data row24 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row24_col5\" class=\"data row24 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row24_col6\" class=\"data row24 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
              "      <td id=\"T_05adb_row25_col1\" class=\"data row25 col1\" >ROC Curve</td>\n",
              "      <td id=\"T_05adb_row25_col2\" class=\"data row25 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
              "      <td id=\"T_05adb_row25_col3\" class=\"data row25 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row25_col4\" class=\"data row25 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row25_col5\" class=\"data row25 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row25_col6\" class=\"data row25 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
              "      <td id=\"T_05adb_row26_col1\" class=\"data row26 col1\" >Regression Errors</td>\n",
              "      <td id=\"T_05adb_row26_col2\" class=\"data row26 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
              "      <td id=\"T_05adb_row26_col3\" class=\"data row26 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row26_col4\" class=\"data row26 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row26_col5\" class=\"data row26 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row26_col6\" class=\"data row26 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
              "      <td id=\"T_05adb_row27_col1\" class=\"data row27 col1\" >Regression Errors Comparison</td>\n",
              "      <td id=\"T_05adb_row27_col2\" class=\"data row27 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
              "      <td id=\"T_05adb_row27_col3\" class=\"data row27 col3\" >['datasets', 'models']</td>\n",
              "      <td id=\"T_05adb_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row27_col5\" class=\"data row27 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_05adb_row27_col6\" class=\"data row27 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
              "      <td id=\"T_05adb_row28_col1\" class=\"data row28 col1\" >Regression Performance</td>\n",
              "      <td id=\"T_05adb_row28_col2\" class=\"data row28 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
              "      <td id=\"T_05adb_row28_col3\" class=\"data row28 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row28_col5\" class=\"data row28 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row28_col6\" class=\"data row28 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
              "      <td id=\"T_05adb_row29_col1\" class=\"data row29 col1\" >Regression R2 Square</td>\n",
              "      <td id=\"T_05adb_row29_col2\" class=\"data row29 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
              "      <td id=\"T_05adb_row29_col3\" class=\"data row29 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row29_col4\" class=\"data row29 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row29_col5\" class=\"data row29 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row29_col6\" class=\"data row29 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
              "      <td id=\"T_05adb_row30_col1\" class=\"data row30 col1\" >Regression R2 Square Comparison</td>\n",
              "      <td id=\"T_05adb_row30_col2\" class=\"data row30 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
              "      <td id=\"T_05adb_row30_col3\" class=\"data row30 col3\" >['datasets', 'models']</td>\n",
              "      <td id=\"T_05adb_row30_col4\" class=\"data row30 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row30_col5\" class=\"data row30 col5\" >['model_performance', 'sklearn']</td>\n",
              "      <td id=\"T_05adb_row30_col6\" class=\"data row30 col6\" >['regression', 'time_series_forecasting']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
              "      <td id=\"T_05adb_row31_col1\" class=\"data row31 col1\" >Robustness Diagnosis</td>\n",
              "      <td id=\"T_05adb_row31_col2\" class=\"data row31 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
              "      <td id=\"T_05adb_row31_col3\" class=\"data row31 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row31_col4\" class=\"data row31 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_05adb_row31_col5\" class=\"data row31 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row31_col6\" class=\"data row31 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
              "      <td id=\"T_05adb_row32_col1\" class=\"data row32 col1\" >SHAP Global Importance</td>\n",
              "      <td id=\"T_05adb_row32_col2\" class=\"data row32 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
              "      <td id=\"T_05adb_row32_col3\" class=\"data row32 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row32_col4\" class=\"data row32 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row32_col5\" class=\"data row32 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row32_col6\" class=\"data row32 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
              "      <td id=\"T_05adb_row33_col1\" class=\"data row33 col1\" >Score Probability Alignment</td>\n",
              "      <td id=\"T_05adb_row33_col2\" class=\"data row33 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
              "      <td id=\"T_05adb_row33_col3\" class=\"data row33 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row33_col4\" class=\"data row33 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_05adb_row33_col5\" class=\"data row33 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
              "      <td id=\"T_05adb_row33_col6\" class=\"data row33 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
              "      <td id=\"T_05adb_row34_col1\" class=\"data row34 col1\" >Silhouette Plot</td>\n",
              "      <td id=\"T_05adb_row34_col2\" class=\"data row34 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
              "      <td id=\"T_05adb_row34_col3\" class=\"data row34 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_05adb_row34_col4\" class=\"data row34 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row34_col5\" class=\"data row34 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row34_col6\" class=\"data row34 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
              "      <td id=\"T_05adb_row35_col1\" class=\"data row35 col1\" >Training Test Degradation</td>\n",
              "      <td id=\"T_05adb_row35_col2\" class=\"data row35 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
              "      <td id=\"T_05adb_row35_col3\" class=\"data row35 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row35_col4\" class=\"data row35 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_05adb_row35_col5\" class=\"data row35 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row35_col6\" class=\"data row35 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
              "      <td id=\"T_05adb_row36_col1\" class=\"data row36 col1\" >V Measure</td>\n",
              "      <td id=\"T_05adb_row36_col2\" class=\"data row36 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
              "      <td id=\"T_05adb_row36_col3\" class=\"data row36 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_05adb_row36_col4\" class=\"data row36 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row36_col5\" class=\"data row36 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row36_col6\" class=\"data row36 col6\" >['clustering']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
              "      <td id=\"T_05adb_row37_col1\" class=\"data row37 col1\" >Weakspots Diagnosis</td>\n",
              "      <td id=\"T_05adb_row37_col2\" class=\"data row37 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
              "      <td id=\"T_05adb_row37_col3\" class=\"data row37 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row37_col4\" class=\"data row37 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_05adb_row37_col5\" class=\"data row37 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row37_col6\" class=\"data row37 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row38_col0\" class=\"data row38 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
              "      <td id=\"T_05adb_row38_col1\" class=\"data row38 col1\" >Calibration Curve Drift</td>\n",
              "      <td id=\"T_05adb_row38_col2\" class=\"data row38 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_05adb_row38_col3\" class=\"data row38 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row38_col4\" class=\"data row38 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
              "      <td id=\"T_05adb_row38_col5\" class=\"data row38 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row38_col6\" class=\"data row38 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row39_col0\" class=\"data row39 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
              "      <td id=\"T_05adb_row39_col1\" class=\"data row39 col1\" >Class Discrimination Drift</td>\n",
              "      <td id=\"T_05adb_row39_col2\" class=\"data row39 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_05adb_row39_col3\" class=\"data row39 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row39_col4\" class=\"data row39 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_05adb_row39_col5\" class=\"data row39 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row39_col6\" class=\"data row39 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row40_col0\" class=\"data row40 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
              "      <td id=\"T_05adb_row40_col1\" class=\"data row40 col1\" >Classification Accuracy Drift</td>\n",
              "      <td id=\"T_05adb_row40_col2\" class=\"data row40 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_05adb_row40_col3\" class=\"data row40 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row40_col4\" class=\"data row40 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_05adb_row40_col5\" class=\"data row40 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row40_col6\" class=\"data row40 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row41_col0\" class=\"data row41 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
              "      <td id=\"T_05adb_row41_col1\" class=\"data row41 col1\" >Confusion Matrix Drift</td>\n",
              "      <td id=\"T_05adb_row41_col2\" class=\"data row41 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_05adb_row41_col3\" class=\"data row41 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row41_col4\" class=\"data row41 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_05adb_row41_col5\" class=\"data row41 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_05adb_row41_col6\" class=\"data row41 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_05adb_row42_col0\" class=\"data row42 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
              "      <td id=\"T_05adb_row42_col1\" class=\"data row42 col1\" >ROC Curve Drift</td>\n",
              "      <td id=\"T_05adb_row42_col2\" class=\"data row42 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_05adb_row42_col3\" class=\"data row42 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_05adb_row42_col4\" class=\"data row42 col4\" >{}</td>\n",
              "      <td id=\"T_05adb_row42_col5\" class=\"data row42 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_05adb_row42_col6\" class=\"data row42 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x3618a9580>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tests(filter=\"sklearn\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `task` parameter is designed for pinpointing tests that align with a specific task type. For instance, to find tests tailored for 'classification' tasks:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
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#T_686e6_row74_col2, #T_686e6_row74_col3, #T_686e6_row74_col4, #T_686e6_row74_col5, #T_686e6_row74_col6, #T_686e6_row75_col0, #T_686e6_row75_col1, #T_686e6_row75_col2, #T_686e6_row75_col3, #T_686e6_row75_col4, #T_686e6_row75_col5, #T_686e6_row75_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_686e6\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_686e6_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
              "      <th id=\"T_686e6_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
              "      <th id=\"T_686e6_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
              "      <th id=\"T_686e6_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
              "      <th id=\"T_686e6_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
              "      <th id=\"T_686e6_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
              "      <th id=\"T_686e6_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
              "      <td id=\"T_686e6_row0_col1\" class=\"data row0 col1\" >Bivariate Scatter Plots</td>\n",
              "      <td id=\"T_686e6_row0_col2\" class=\"data row0 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
              "      <td id=\"T_686e6_row0_col3\" class=\"data row0 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row0_col5\" class=\"data row0 col5\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row0_col6\" class=\"data row0 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
              "      <td id=\"T_686e6_row1_col1\" class=\"data row1 col1\" >Chi Squared Features Table</td>\n",
              "      <td id=\"T_686e6_row1_col2\" class=\"data row1 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
              "      <td id=\"T_686e6_row1_col3\" class=\"data row1 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row1_col4\" class=\"data row1 col4\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
              "      <td id=\"T_686e6_row1_col5\" class=\"data row1 col5\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
              "      <td id=\"T_686e6_row1_col6\" class=\"data row1 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.ClassImbalance</td>\n",
              "      <td id=\"T_686e6_row2_col1\" class=\"data row2 col1\" >Class Imbalance</td>\n",
              "      <td id=\"T_686e6_row2_col2\" class=\"data row2 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
              "      <td id=\"T_686e6_row2_col3\" class=\"data row2 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row2_col4\" class=\"data row2 col4\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_686e6_row2_col5\" class=\"data row2 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
              "      <td id=\"T_686e6_row2_col6\" class=\"data row2 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.DatasetDescription</td>\n",
              "      <td id=\"T_686e6_row3_col1\" class=\"data row3 col1\" >Dataset Description</td>\n",
              "      <td id=\"T_686e6_row3_col2\" class=\"data row3 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
              "      <td id=\"T_686e6_row3_col3\" class=\"data row3 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row3_col5\" class=\"data row3 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
              "      <td id=\"T_686e6_row3_col6\" class=\"data row3 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.DatasetSplit</td>\n",
              "      <td id=\"T_686e6_row4_col1\" class=\"data row4 col1\" >Dataset Split</td>\n",
              "      <td id=\"T_686e6_row4_col2\" class=\"data row4 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
              "      <td id=\"T_686e6_row4_col3\" class=\"data row4 col3\" >['datasets']</td>\n",
              "      <td id=\"T_686e6_row4_col4\" class=\"data row4 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row4_col5\" class=\"data row4 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
              "      <td id=\"T_686e6_row4_col6\" class=\"data row4 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
              "      <td id=\"T_686e6_row5_col1\" class=\"data row5 col1\" >Descriptive Statistics</td>\n",
              "      <td id=\"T_686e6_row5_col2\" class=\"data row5 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
              "      <td id=\"T_686e6_row5_col3\" class=\"data row5 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row5_col5\" class=\"data row5 col5\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
              "      <td id=\"T_686e6_row5_col6\" class=\"data row5 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.Duplicates</td>\n",
              "      <td id=\"T_686e6_row6_col1\" class=\"data row6 col1\" >Duplicates</td>\n",
              "      <td id=\"T_686e6_row6_col2\" class=\"data row6 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
              "      <td id=\"T_686e6_row6_col3\" class=\"data row6 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row6_col4\" class=\"data row6 col4\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
              "      <td id=\"T_686e6_row6_col5\" class=\"data row6 col5\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
              "      <td id=\"T_686e6_row6_col6\" class=\"data row6 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
              "      <td id=\"T_686e6_row7_col1\" class=\"data row7 col1\" >Feature Target Correlation Plot</td>\n",
              "      <td id=\"T_686e6_row7_col2\" class=\"data row7 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
              "      <td id=\"T_686e6_row7_col3\" class=\"data row7 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row7_col4\" class=\"data row7 col4\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
              "      <td id=\"T_686e6_row7_col5\" class=\"data row7 col5\" >['tabular_data', 'visualization', 'correlation']</td>\n",
              "      <td id=\"T_686e6_row7_col6\" class=\"data row7 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.HighCardinality</td>\n",
              "      <td id=\"T_686e6_row8_col1\" class=\"data row8 col1\" >High Cardinality</td>\n",
              "      <td id=\"T_686e6_row8_col2\" class=\"data row8 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
              "      <td id=\"T_686e6_row8_col3\" class=\"data row8 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row8_col4\" class=\"data row8 col4\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
              "      <td id=\"T_686e6_row8_col5\" class=\"data row8 col5\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
              "      <td id=\"T_686e6_row8_col6\" class=\"data row8 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
              "      <td id=\"T_686e6_row9_col1\" class=\"data row9 col1\" >High Pearson Correlation</td>\n",
              "      <td id=\"T_686e6_row9_col2\" class=\"data row9 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
              "      <td id=\"T_686e6_row9_col3\" class=\"data row9 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row9_col4\" class=\"data row9 col4\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row9_col5\" class=\"data row9 col5\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
              "      <td id=\"T_686e6_row9_col6\" class=\"data row9 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
              "      <td id=\"T_686e6_row10_col1\" class=\"data row10 col1\" >IQR Outliers Bar Plot</td>\n",
              "      <td id=\"T_686e6_row10_col2\" class=\"data row10 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
              "      <td id=\"T_686e6_row10_col3\" class=\"data row10 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row10_col4\" class=\"data row10 col4\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
              "      <td id=\"T_686e6_row10_col5\" class=\"data row10 col5\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
              "      <td id=\"T_686e6_row10_col6\" class=\"data row10 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
              "      <td id=\"T_686e6_row11_col1\" class=\"data row11 col1\" >IQR Outliers Table</td>\n",
              "      <td id=\"T_686e6_row11_col2\" class=\"data row11 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
              "      <td id=\"T_686e6_row11_col3\" class=\"data row11 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row11_col4\" class=\"data row11 col4\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
              "      <td id=\"T_686e6_row11_col5\" class=\"data row11 col5\" >['tabular_data', 'numerical_data']</td>\n",
              "      <td id=\"T_686e6_row11_col6\" class=\"data row11 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
              "      <td id=\"T_686e6_row12_col1\" class=\"data row12 col1\" >Isolation Forest Outliers</td>\n",
              "      <td id=\"T_686e6_row12_col2\" class=\"data row12 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
              "      <td id=\"T_686e6_row12_col3\" class=\"data row12 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row12_col4\" class=\"data row12 col4\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row12_col5\" class=\"data row12 col5\" >['tabular_data', 'anomaly_detection']</td>\n",
              "      <td id=\"T_686e6_row12_col6\" class=\"data row12 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.JarqueBera</td>\n",
              "      <td id=\"T_686e6_row13_col1\" class=\"data row13 col1\" >Jarque Bera</td>\n",
              "      <td id=\"T_686e6_row13_col2\" class=\"data row13 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
              "      <td id=\"T_686e6_row13_col3\" class=\"data row13 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row13_col4\" class=\"data row13 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row13_col5\" class=\"data row13 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_686e6_row13_col6\" class=\"data row13 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.MissingValues</td>\n",
              "      <td id=\"T_686e6_row14_col1\" class=\"data row14 col1\" >Missing Values</td>\n",
              "      <td id=\"T_686e6_row14_col2\" class=\"data row14 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
              "      <td id=\"T_686e6_row14_col3\" class=\"data row14 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row14_col4\" class=\"data row14 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
              "      <td id=\"T_686e6_row14_col5\" class=\"data row14 col5\" >['tabular_data', 'data_quality']</td>\n",
              "      <td id=\"T_686e6_row14_col6\" class=\"data row14 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
              "      <td id=\"T_686e6_row15_col1\" class=\"data row15 col1\" >Missing Values Bar Plot</td>\n",
              "      <td id=\"T_686e6_row15_col2\" class=\"data row15 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
              "      <td id=\"T_686e6_row15_col3\" class=\"data row15 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row15_col4\" class=\"data row15 col4\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
              "      <td id=\"T_686e6_row15_col5\" class=\"data row15 col5\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row15_col6\" class=\"data row15 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.MutualInformation</td>\n",
              "      <td id=\"T_686e6_row16_col1\" class=\"data row16 col1\" >Mutual Information</td>\n",
              "      <td id=\"T_686e6_row16_col2\" class=\"data row16 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
              "      <td id=\"T_686e6_row16_col3\" class=\"data row16 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row16_col4\" class=\"data row16 col4\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
              "      <td id=\"T_686e6_row16_col5\" class=\"data row16 col5\" >['feature_selection', 'data_analysis']</td>\n",
              "      <td id=\"T_686e6_row16_col6\" class=\"data row16 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
              "      <td id=\"T_686e6_row17_col1\" class=\"data row17 col1\" >Pearson Correlation Matrix</td>\n",
              "      <td id=\"T_686e6_row17_col2\" class=\"data row17 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
              "      <td id=\"T_686e6_row17_col3\" class=\"data row17 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row17_col4\" class=\"data row17 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row17_col5\" class=\"data row17 col5\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
              "      <td id=\"T_686e6_row17_col6\" class=\"data row17 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
              "      <td id=\"T_686e6_row18_col1\" class=\"data row18 col1\" >Protected Classes Description</td>\n",
              "      <td id=\"T_686e6_row18_col2\" class=\"data row18 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
              "      <td id=\"T_686e6_row18_col3\" class=\"data row18 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row18_col4\" class=\"data row18 col4\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row18_col5\" class=\"data row18 col5\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
              "      <td id=\"T_686e6_row18_col6\" class=\"data row18 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.RunsTest</td>\n",
              "      <td id=\"T_686e6_row19_col1\" class=\"data row19 col1\" >Runs Test</td>\n",
              "      <td id=\"T_686e6_row19_col2\" class=\"data row19 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
              "      <td id=\"T_686e6_row19_col3\" class=\"data row19 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row19_col4\" class=\"data row19 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row19_col5\" class=\"data row19 col5\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_686e6_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.ScatterPlot</td>\n",
              "      <td id=\"T_686e6_row20_col1\" class=\"data row20 col1\" >Scatter Plot</td>\n",
              "      <td id=\"T_686e6_row20_col2\" class=\"data row20 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
              "      <td id=\"T_686e6_row20_col3\" class=\"data row20 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row20_col4\" class=\"data row20 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row20_col5\" class=\"data row20 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row20_col6\" class=\"data row20 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
              "      <td id=\"T_686e6_row21_col1\" class=\"data row21 col1\" >Score Band Default Rates</td>\n",
              "      <td id=\"T_686e6_row21_col2\" class=\"data row21 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
              "      <td id=\"T_686e6_row21_col3\" class=\"data row21 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row21_col4\" class=\"data row21 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row21_col5\" class=\"data row21 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
              "      <td id=\"T_686e6_row21_col6\" class=\"data row21 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
              "      <td id=\"T_686e6_row22_col1\" class=\"data row22 col1\" >Shapiro Wilk</td>\n",
              "      <td id=\"T_686e6_row22_col2\" class=\"data row22 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
              "      <td id=\"T_686e6_row22_col3\" class=\"data row22 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row22_col4\" class=\"data row22 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row22_col5\" class=\"data row22 col5\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
              "      <td id=\"T_686e6_row22_col6\" class=\"data row22 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.Skewness</td>\n",
              "      <td id=\"T_686e6_row23_col1\" class=\"data row23 col1\" >Skewness</td>\n",
              "      <td id=\"T_686e6_row23_col2\" class=\"data row23 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
              "      <td id=\"T_686e6_row23_col3\" class=\"data row23 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row23_col4\" class=\"data row23 col4\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
              "      <td id=\"T_686e6_row23_col5\" class=\"data row23 col5\" >['data_quality', 'tabular_data']</td>\n",
              "      <td id=\"T_686e6_row23_col6\" class=\"data row23 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
              "      <td id=\"T_686e6_row24_col1\" class=\"data row24 col1\" >Tabular Categorical Bar Plots</td>\n",
              "      <td id=\"T_686e6_row24_col2\" class=\"data row24 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
              "      <td id=\"T_686e6_row24_col3\" class=\"data row24 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row24_col4\" class=\"data row24 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row24_col5\" class=\"data row24 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row24_col6\" class=\"data row24 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
              "      <td id=\"T_686e6_row25_col1\" class=\"data row25 col1\" >Tabular Date Time Histograms</td>\n",
              "      <td id=\"T_686e6_row25_col2\" class=\"data row25 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
              "      <td id=\"T_686e6_row25_col3\" class=\"data row25 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row25_col4\" class=\"data row25 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row25_col5\" class=\"data row25 col5\" >['time_series_data', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row25_col6\" class=\"data row25 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
              "      <td id=\"T_686e6_row26_col1\" class=\"data row26 col1\" >Tabular Description Tables</td>\n",
              "      <td id=\"T_686e6_row26_col2\" class=\"data row26 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
              "      <td id=\"T_686e6_row26_col3\" class=\"data row26 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row26_col4\" class=\"data row26 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row26_col5\" class=\"data row26 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_686e6_row26_col6\" class=\"data row26 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
              "      <td id=\"T_686e6_row27_col1\" class=\"data row27 col1\" >Tabular Numerical Histograms</td>\n",
              "      <td id=\"T_686e6_row27_col2\" class=\"data row27 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
              "      <td id=\"T_686e6_row27_col3\" class=\"data row27 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row27_col5\" class=\"data row27 col5\" >['tabular_data', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row27_col6\" class=\"data row27 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
              "      <td id=\"T_686e6_row28_col1\" class=\"data row28 col1\" >Target Rate Bar Plots</td>\n",
              "      <td id=\"T_686e6_row28_col2\" class=\"data row28 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
              "      <td id=\"T_686e6_row28_col3\" class=\"data row28 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row28_col5\" class=\"data row28 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
              "      <td id=\"T_686e6_row28_col6\" class=\"data row28 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
              "      <td id=\"T_686e6_row29_col1\" class=\"data row29 col1\" >Too Many Zero Values</td>\n",
              "      <td id=\"T_686e6_row29_col2\" class=\"data row29 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
              "      <td id=\"T_686e6_row29_col3\" class=\"data row29 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row29_col4\" class=\"data row29 col4\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
              "      <td id=\"T_686e6_row29_col5\" class=\"data row29 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_686e6_row29_col6\" class=\"data row29 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.UniqueRows</td>\n",
              "      <td id=\"T_686e6_row30_col1\" class=\"data row30 col1\" >Unique Rows</td>\n",
              "      <td id=\"T_686e6_row30_col2\" class=\"data row30 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
              "      <td id=\"T_686e6_row30_col3\" class=\"data row30 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row30_col4\" class=\"data row30 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
              "      <td id=\"T_686e6_row30_col5\" class=\"data row30 col5\" >['tabular_data']</td>\n",
              "      <td id=\"T_686e6_row30_col6\" class=\"data row30 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
              "      <td id=\"T_686e6_row31_col1\" class=\"data row31 col1\" >WOE Bin Plots</td>\n",
              "      <td id=\"T_686e6_row31_col2\" class=\"data row31 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
              "      <td id=\"T_686e6_row31_col3\" class=\"data row31 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row31_col4\" class=\"data row31 col4\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
              "      <td id=\"T_686e6_row31_col5\" class=\"data row31 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
              "      <td id=\"T_686e6_row31_col6\" class=\"data row31 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.WOEBinTable</td>\n",
              "      <td id=\"T_686e6_row32_col1\" class=\"data row32 col1\" >WOE Bin Table</td>\n",
              "      <td id=\"T_686e6_row32_col2\" class=\"data row32 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
              "      <td id=\"T_686e6_row32_col3\" class=\"data row32 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row32_col4\" class=\"data row32 col4\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row32_col5\" class=\"data row32 col5\" >['tabular_data', 'categorical_data']</td>\n",
              "      <td id=\"T_686e6_row32_col6\" class=\"data row32 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
              "      <td id=\"T_686e6_row33_col1\" class=\"data row33 col1\" >Features AUC</td>\n",
              "      <td id=\"T_686e6_row33_col2\" class=\"data row33 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
              "      <td id=\"T_686e6_row33_col3\" class=\"data row33 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row33_col4\" class=\"data row33 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
              "      <td id=\"T_686e6_row33_col5\" class=\"data row33 col5\" >['feature_importance', 'AUC', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row33_col6\" class=\"data row33 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
              "      <td id=\"T_686e6_row34_col1\" class=\"data row34 col1\" >Calibration Curve</td>\n",
              "      <td id=\"T_686e6_row34_col2\" class=\"data row34 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
              "      <td id=\"T_686e6_row34_col3\" class=\"data row34 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row34_col4\" class=\"data row34 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_686e6_row34_col5\" class=\"data row34 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
              "      <td id=\"T_686e6_row34_col6\" class=\"data row34 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
              "      <td id=\"T_686e6_row35_col1\" class=\"data row35 col1\" >Classifier Performance</td>\n",
              "      <td id=\"T_686e6_row35_col2\" class=\"data row35 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
              "      <td id=\"T_686e6_row35_col3\" class=\"data row35 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row35_col4\" class=\"data row35 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
              "      <td id=\"T_686e6_row35_col5\" class=\"data row35 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row35_col6\" class=\"data row35 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
              "      <td id=\"T_686e6_row36_col1\" class=\"data row36 col1\" >Classifier Threshold Optimization</td>\n",
              "      <td id=\"T_686e6_row36_col2\" class=\"data row36 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
              "      <td id=\"T_686e6_row36_col3\" class=\"data row36 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row36_col4\" class=\"data row36 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row36_col5\" class=\"data row36 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
              "      <td id=\"T_686e6_row36_col6\" class=\"data row36 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
              "      <td id=\"T_686e6_row37_col1\" class=\"data row37 col1\" >Confusion Matrix</td>\n",
              "      <td id=\"T_686e6_row37_col2\" class=\"data row37 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
              "      <td id=\"T_686e6_row37_col3\" class=\"data row37 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row37_col4\" class=\"data row37 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_686e6_row37_col5\" class=\"data row37 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row37_col6\" class=\"data row37 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row38_col0\" class=\"data row38 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
              "      <td id=\"T_686e6_row38_col1\" class=\"data row38 col1\" >Hyper Parameters Tuning</td>\n",
              "      <td id=\"T_686e6_row38_col2\" class=\"data row38 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
              "      <td id=\"T_686e6_row38_col3\" class=\"data row38 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row38_col4\" class=\"data row38 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row38_col5\" class=\"data row38 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row38_col6\" class=\"data row38 col6\" >['clustering', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row39_col0\" class=\"data row39 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
              "      <td id=\"T_686e6_row39_col1\" class=\"data row39 col1\" >Minimum Accuracy</td>\n",
              "      <td id=\"T_686e6_row39_col2\" class=\"data row39 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_686e6_row39_col3\" class=\"data row39 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row39_col4\" class=\"data row39 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
              "      <td id=\"T_686e6_row39_col5\" class=\"data row39 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row39_col6\" class=\"data row39 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row40_col0\" class=\"data row40 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
              "      <td id=\"T_686e6_row40_col1\" class=\"data row40 col1\" >Minimum F1 Score</td>\n",
              "      <td id=\"T_686e6_row40_col2\" class=\"data row40 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
              "      <td id=\"T_686e6_row40_col3\" class=\"data row40 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row40_col4\" class=\"data row40 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_686e6_row40_col5\" class=\"data row40 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row40_col6\" class=\"data row40 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row41_col0\" class=\"data row41 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
              "      <td id=\"T_686e6_row41_col1\" class=\"data row41 col1\" >Minimum ROCAUC Score</td>\n",
              "      <td id=\"T_686e6_row41_col2\" class=\"data row41 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
              "      <td id=\"T_686e6_row41_col3\" class=\"data row41 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row41_col4\" class=\"data row41 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_686e6_row41_col5\" class=\"data row41 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row41_col6\" class=\"data row41 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row42_col0\" class=\"data row42 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
              "      <td id=\"T_686e6_row42_col1\" class=\"data row42 col1\" >Model Parameters</td>\n",
              "      <td id=\"T_686e6_row42_col2\" class=\"data row42 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
              "      <td id=\"T_686e6_row42_col3\" class=\"data row42 col3\" >['model']</td>\n",
              "      <td id=\"T_686e6_row42_col4\" class=\"data row42 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row42_col5\" class=\"data row42 col5\" >['model_training', 'metadata']</td>\n",
              "      <td id=\"T_686e6_row42_col6\" class=\"data row42 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row43_col0\" class=\"data row43 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
              "      <td id=\"T_686e6_row43_col1\" class=\"data row43 col1\" >Models Performance Comparison</td>\n",
              "      <td id=\"T_686e6_row43_col2\" class=\"data row43 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
              "      <td id=\"T_686e6_row43_col3\" class=\"data row43 col3\" >['dataset', 'models']</td>\n",
              "      <td id=\"T_686e6_row43_col4\" class=\"data row43 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row43_col5\" class=\"data row43 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
              "      <td id=\"T_686e6_row43_col6\" class=\"data row43 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row44_col0\" class=\"data row44 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
              "      <td id=\"T_686e6_row44_col1\" class=\"data row44 col1\" >Overfit Diagnosis</td>\n",
              "      <td id=\"T_686e6_row44_col2\" class=\"data row44 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
              "      <td id=\"T_686e6_row44_col3\" class=\"data row44 col3\" >['model', 'datasets']</td>\n",
              "      <td id=\"T_686e6_row44_col4\" class=\"data row44 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
              "      <td id=\"T_686e6_row44_col5\" class=\"data row44 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
              "      <td id=\"T_686e6_row44_col6\" class=\"data row44 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row45_col0\" class=\"data row45 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
              "      <td id=\"T_686e6_row45_col1\" class=\"data row45 col1\" >Permutation Feature Importance</td>\n",
              "      <td id=\"T_686e6_row45_col2\" class=\"data row45 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
              "      <td id=\"T_686e6_row45_col3\" class=\"data row45 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row45_col4\" class=\"data row45 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row45_col5\" class=\"data row45 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row45_col6\" class=\"data row45 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row46_col0\" class=\"data row46 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
              "      <td id=\"T_686e6_row46_col1\" class=\"data row46 col1\" >Population Stability Index</td>\n",
              "      <td id=\"T_686e6_row46_col2\" class=\"data row46 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
              "      <td id=\"T_686e6_row46_col3\" class=\"data row46 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row46_col4\" class=\"data row46 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
              "      <td id=\"T_686e6_row46_col5\" class=\"data row46 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row46_col6\" class=\"data row46 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row47_col0\" class=\"data row47 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
              "      <td id=\"T_686e6_row47_col1\" class=\"data row47 col1\" >Precision Recall Curve</td>\n",
              "      <td id=\"T_686e6_row47_col2\" class=\"data row47 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
              "      <td id=\"T_686e6_row47_col3\" class=\"data row47 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row47_col4\" class=\"data row47 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row47_col5\" class=\"data row47 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row47_col6\" class=\"data row47 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row48_col0\" class=\"data row48 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
              "      <td id=\"T_686e6_row48_col1\" class=\"data row48 col1\" >ROC Curve</td>\n",
              "      <td id=\"T_686e6_row48_col2\" class=\"data row48 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
              "      <td id=\"T_686e6_row48_col3\" class=\"data row48 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row48_col4\" class=\"data row48 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row48_col5\" class=\"data row48 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row48_col6\" class=\"data row48 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row49_col0\" class=\"data row49 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
              "      <td id=\"T_686e6_row49_col1\" class=\"data row49 col1\" >Regression Errors</td>\n",
              "      <td id=\"T_686e6_row49_col2\" class=\"data row49 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
              "      <td id=\"T_686e6_row49_col3\" class=\"data row49 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row49_col4\" class=\"data row49 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row49_col5\" class=\"data row49 col5\" >['sklearn', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row49_col6\" class=\"data row49 col6\" >['regression', 'classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row50_col0\" class=\"data row50 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
              "      <td id=\"T_686e6_row50_col1\" class=\"data row50 col1\" >Robustness Diagnosis</td>\n",
              "      <td id=\"T_686e6_row50_col2\" class=\"data row50 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
              "      <td id=\"T_686e6_row50_col3\" class=\"data row50 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row50_col4\" class=\"data row50 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
              "      <td id=\"T_686e6_row50_col5\" class=\"data row50 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row50_col6\" class=\"data row50 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row51_col0\" class=\"data row51 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
              "      <td id=\"T_686e6_row51_col1\" class=\"data row51 col1\" >SHAP Global Importance</td>\n",
              "      <td id=\"T_686e6_row51_col2\" class=\"data row51 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
              "      <td id=\"T_686e6_row51_col3\" class=\"data row51 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row51_col4\" class=\"data row51 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row51_col5\" class=\"data row51 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row51_col6\" class=\"data row51 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row52_col0\" class=\"data row52 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
              "      <td id=\"T_686e6_row52_col1\" class=\"data row52 col1\" >Score Probability Alignment</td>\n",
              "      <td id=\"T_686e6_row52_col2\" class=\"data row52 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
              "      <td id=\"T_686e6_row52_col3\" class=\"data row52 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row52_col4\" class=\"data row52 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
              "      <td id=\"T_686e6_row52_col5\" class=\"data row52 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
              "      <td id=\"T_686e6_row52_col6\" class=\"data row52 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row53_col0\" class=\"data row53 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
              "      <td id=\"T_686e6_row53_col1\" class=\"data row53 col1\" >Training Test Degradation</td>\n",
              "      <td id=\"T_686e6_row53_col2\" class=\"data row53 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
              "      <td id=\"T_686e6_row53_col3\" class=\"data row53 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row53_col4\" class=\"data row53 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_686e6_row53_col5\" class=\"data row53 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row53_col6\" class=\"data row53 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row54_col0\" class=\"data row54 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
              "      <td id=\"T_686e6_row54_col1\" class=\"data row54 col1\" >Weakspots Diagnosis</td>\n",
              "      <td id=\"T_686e6_row54_col2\" class=\"data row54 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
              "      <td id=\"T_686e6_row54_col3\" class=\"data row54 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row54_col4\" class=\"data row54 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
              "      <td id=\"T_686e6_row54_col5\" class=\"data row54 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row54_col6\" class=\"data row54 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row55_col0\" class=\"data row55 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
              "      <td id=\"T_686e6_row55_col1\" class=\"data row55 col1\" >Cumulative Prediction Probabilities</td>\n",
              "      <td id=\"T_686e6_row55_col2\" class=\"data row55 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
              "      <td id=\"T_686e6_row55_col3\" class=\"data row55 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row55_col4\" class=\"data row55 col4\" >{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}</td>\n",
              "      <td id=\"T_686e6_row55_col5\" class=\"data row55 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_686e6_row55_col6\" class=\"data row55 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row56_col0\" class=\"data row56 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
              "      <td id=\"T_686e6_row56_col1\" class=\"data row56 col1\" >GINI Table</td>\n",
              "      <td id=\"T_686e6_row56_col2\" class=\"data row56 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
              "      <td id=\"T_686e6_row56_col3\" class=\"data row56 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row56_col4\" class=\"data row56 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row56_col5\" class=\"data row56 col5\" >['model_performance']</td>\n",
              "      <td id=\"T_686e6_row56_col6\" class=\"data row56 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row57_col0\" class=\"data row57 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
              "      <td id=\"T_686e6_row57_col1\" class=\"data row57 col1\" >Kolmogorov Smirnov</td>\n",
              "      <td id=\"T_686e6_row57_col2\" class=\"data row57 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
              "      <td id=\"T_686e6_row57_col3\" class=\"data row57 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row57_col4\" class=\"data row57 col4\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
              "      <td id=\"T_686e6_row57_col5\" class=\"data row57 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_686e6_row57_col6\" class=\"data row57 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row58_col0\" class=\"data row58 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
              "      <td id=\"T_686e6_row58_col1\" class=\"data row58 col1\" >Lilliefors</td>\n",
              "      <td id=\"T_686e6_row58_col2\" class=\"data row58 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
              "      <td id=\"T_686e6_row58_col3\" class=\"data row58 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row58_col4\" class=\"data row58 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row58_col5\" class=\"data row58 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
              "      <td id=\"T_686e6_row58_col6\" class=\"data row58 col6\" >['classification', 'regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row59_col0\" class=\"data row59 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
              "      <td id=\"T_686e6_row59_col1\" class=\"data row59 col1\" >Prediction Probabilities Histogram</td>\n",
              "      <td id=\"T_686e6_row59_col2\" class=\"data row59 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
              "      <td id=\"T_686e6_row59_col3\" class=\"data row59 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row59_col4\" class=\"data row59 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
              "      <td id=\"T_686e6_row59_col5\" class=\"data row59 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_686e6_row59_col6\" class=\"data row59 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row60_col0\" class=\"data row60 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
              "      <td id=\"T_686e6_row60_col1\" class=\"data row60 col1\" >Scorecard Histogram</td>\n",
              "      <td id=\"T_686e6_row60_col2\" class=\"data row60 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
              "      <td id=\"T_686e6_row60_col3\" class=\"data row60 col3\" >['dataset']</td>\n",
              "      <td id=\"T_686e6_row60_col4\" class=\"data row60 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}</td>\n",
              "      <td id=\"T_686e6_row60_col5\" class=\"data row60 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
              "      <td id=\"T_686e6_row60_col6\" class=\"data row60 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row61_col0\" class=\"data row61 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
              "      <td id=\"T_686e6_row61_col1\" class=\"data row61 col1\" >Calibration Curve Drift</td>\n",
              "      <td id=\"T_686e6_row61_col2\" class=\"data row61 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row61_col3\" class=\"data row61 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row61_col4\" class=\"data row61 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
              "      <td id=\"T_686e6_row61_col5\" class=\"data row61 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row61_col6\" class=\"data row61 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row62_col0\" class=\"data row62 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
              "      <td id=\"T_686e6_row62_col1\" class=\"data row62 col1\" >Class Discrimination Drift</td>\n",
              "      <td id=\"T_686e6_row62_col2\" class=\"data row62 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row62_col3\" class=\"data row62 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row62_col4\" class=\"data row62 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_686e6_row62_col5\" class=\"data row62 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row62_col6\" class=\"data row62 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row63_col0\" class=\"data row63 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
              "      <td id=\"T_686e6_row63_col1\" class=\"data row63 col1\" >Class Imbalance Drift</td>\n",
              "      <td id=\"T_686e6_row63_col2\" class=\"data row63 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row63_col3\" class=\"data row63 col3\" >['datasets']</td>\n",
              "      <td id=\"T_686e6_row63_col4\" class=\"data row63 col4\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
              "      <td id=\"T_686e6_row63_col5\" class=\"data row63 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
              "      <td id=\"T_686e6_row63_col6\" class=\"data row63 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row64_col0\" class=\"data row64 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
              "      <td id=\"T_686e6_row64_col1\" class=\"data row64 col1\" >Classification Accuracy Drift</td>\n",
              "      <td id=\"T_686e6_row64_col2\" class=\"data row64 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row64_col3\" class=\"data row64 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row64_col4\" class=\"data row64 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_686e6_row64_col5\" class=\"data row64 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row64_col6\" class=\"data row64 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row65_col0\" class=\"data row65 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
              "      <td id=\"T_686e6_row65_col1\" class=\"data row65 col1\" >Confusion Matrix Drift</td>\n",
              "      <td id=\"T_686e6_row65_col2\" class=\"data row65 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row65_col3\" class=\"data row65 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row65_col4\" class=\"data row65 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
              "      <td id=\"T_686e6_row65_col5\" class=\"data row65 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
              "      <td id=\"T_686e6_row65_col6\" class=\"data row65 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row66_col0\" class=\"data row66 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
              "      <td id=\"T_686e6_row66_col1\" class=\"data row66 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
              "      <td id=\"T_686e6_row66_col2\" class=\"data row66 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row66_col3\" class=\"data row66 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row66_col4\" class=\"data row66 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row66_col5\" class=\"data row66 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_686e6_row66_col6\" class=\"data row66 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row67_col0\" class=\"data row67 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
              "      <td id=\"T_686e6_row67_col1\" class=\"data row67 col1\" >Prediction Probabilities Histogram Drift</td>\n",
              "      <td id=\"T_686e6_row67_col2\" class=\"data row67 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row67_col3\" class=\"data row67 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row67_col4\" class=\"data row67 col4\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_686e6_row67_col5\" class=\"data row67 col5\" >['visualization', 'credit_risk']</td>\n",
              "      <td id=\"T_686e6_row67_col6\" class=\"data row67 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row68_col0\" class=\"data row68 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
              "      <td id=\"T_686e6_row68_col1\" class=\"data row68 col1\" >ROC Curve Drift</td>\n",
              "      <td id=\"T_686e6_row68_col2\" class=\"data row68 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_686e6_row68_col3\" class=\"data row68 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row68_col4\" class=\"data row68 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row68_col5\" class=\"data row68 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_686e6_row68_col6\" class=\"data row68 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row69_col0\" class=\"data row69 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
              "      <td id=\"T_686e6_row69_col1\" class=\"data row69 col1\" >Score Bands Drift</td>\n",
              "      <td id=\"T_686e6_row69_col2\" class=\"data row69 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
              "      <td id=\"T_686e6_row69_col3\" class=\"data row69 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_686e6_row69_col4\" class=\"data row69 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_686e6_row69_col5\" class=\"data row69 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
              "      <td id=\"T_686e6_row69_col6\" class=\"data row69 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row70_col0\" class=\"data row70 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
              "      <td id=\"T_686e6_row70_col1\" class=\"data row70 col1\" >Scorecard Histogram Drift</td>\n",
              "      <td id=\"T_686e6_row70_col2\" class=\"data row70 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
              "      <td id=\"T_686e6_row70_col3\" class=\"data row70 col3\" >['datasets']</td>\n",
              "      <td id=\"T_686e6_row70_col4\" class=\"data row70 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
              "      <td id=\"T_686e6_row70_col5\" class=\"data row70 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
              "      <td id=\"T_686e6_row70_col6\" class=\"data row70 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row71_col0\" class=\"data row71 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
              "      <td id=\"T_686e6_row71_col1\" class=\"data row71 col1\" >Accuracy</td>\n",
              "      <td id=\"T_686e6_row71_col2\" class=\"data row71 col2\" >Calculates the accuracy of a model</td>\n",
              "      <td id=\"T_686e6_row71_col3\" class=\"data row71 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_686e6_row71_col4\" class=\"data row71 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row71_col5\" class=\"data row71 col5\" >['classification']</td>\n",
              "      <td id=\"T_686e6_row71_col6\" class=\"data row71 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row72_col0\" class=\"data row72 col0\" >validmind.unit_metrics.classification.F1</td>\n",
              "      <td id=\"T_686e6_row72_col1\" class=\"data row72 col1\" >F1</td>\n",
              "      <td id=\"T_686e6_row72_col2\" class=\"data row72 col2\" >Calculates the F1 score for a classification model.</td>\n",
              "      <td id=\"T_686e6_row72_col3\" class=\"data row72 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row72_col4\" class=\"data row72 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row72_col5\" class=\"data row72 col5\" >['classification']</td>\n",
              "      <td id=\"T_686e6_row72_col6\" class=\"data row72 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row73_col0\" class=\"data row73 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
              "      <td id=\"T_686e6_row73_col1\" class=\"data row73 col1\" >Precision</td>\n",
              "      <td id=\"T_686e6_row73_col2\" class=\"data row73 col2\" >Calculates the precision for a classification model.</td>\n",
              "      <td id=\"T_686e6_row73_col3\" class=\"data row73 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row73_col4\" class=\"data row73 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row73_col5\" class=\"data row73 col5\" >['classification']</td>\n",
              "      <td id=\"T_686e6_row73_col6\" class=\"data row73 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row74_col0\" class=\"data row74 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
              "      <td id=\"T_686e6_row74_col1\" class=\"data row74 col1\" >ROC AUC</td>\n",
              "      <td id=\"T_686e6_row74_col2\" class=\"data row74 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
              "      <td id=\"T_686e6_row74_col3\" class=\"data row74 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row74_col4\" class=\"data row74 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row74_col5\" class=\"data row74 col5\" >['classification']</td>\n",
              "      <td id=\"T_686e6_row74_col6\" class=\"data row74 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_686e6_row75_col0\" class=\"data row75 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
              "      <td id=\"T_686e6_row75_col1\" class=\"data row75 col1\" >Recall</td>\n",
              "      <td id=\"T_686e6_row75_col2\" class=\"data row75 col2\" >Calculates the recall for a classification model.</td>\n",
              "      <td id=\"T_686e6_row75_col3\" class=\"data row75 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_686e6_row75_col4\" class=\"data row75 col4\" >{}</td>\n",
              "      <td id=\"T_686e6_row75_col5\" class=\"data row75 col5\" >['classification']</td>\n",
              "      <td id=\"T_686e6_row75_col6\" class=\"data row75 col6\" >['classification']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x103a27bb0>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tests(task=\"classification\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `tags` parameter facilitates searching tests by their tags. For instance, if you're interested in only tests associated designed for `model_performance` that produce a plot (denoted by the `visualization` tag)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table id=\"T_5df1f\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_5df1f_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
              "      <th id=\"T_5df1f_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
              "      <th id=\"T_5df1f_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
              "      <th id=\"T_5df1f_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
              "      <th id=\"T_5df1f_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
              "      <th id=\"T_5df1f_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
              "      <th id=\"T_5df1f_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
              "      <td id=\"T_5df1f_row0_col1\" class=\"data row0 col1\" >Regression Residuals Plot</td>\n",
              "      <td id=\"T_5df1f_row0_col2\" class=\"data row0 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
              "      <td id=\"T_5df1f_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_5df1f_row0_col4\" class=\"data row0 col4\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_5df1f_row0_col5\" class=\"data row0 col5\" >['model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row0_col6\" class=\"data row0 col6\" >['regression']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
              "      <td id=\"T_5df1f_row1_col1\" class=\"data row1 col1\" >Confusion Matrix</td>\n",
              "      <td id=\"T_5df1f_row1_col2\" class=\"data row1 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
              "      <td id=\"T_5df1f_row1_col3\" class=\"data row1 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_5df1f_row1_col4\" class=\"data row1 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_5df1f_row1_col5\" class=\"data row1 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row1_col6\" class=\"data row1 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
              "      <td id=\"T_5df1f_row2_col1\" class=\"data row2 col1\" >Precision Recall Curve</td>\n",
              "      <td id=\"T_5df1f_row2_col2\" class=\"data row2 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
              "      <td id=\"T_5df1f_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_5df1f_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
              "      <td id=\"T_5df1f_row2_col5\" class=\"data row2 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row2_col6\" class=\"data row2 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
              "      <td id=\"T_5df1f_row3_col1\" class=\"data row3 col1\" >ROC Curve</td>\n",
              "      <td id=\"T_5df1f_row3_col2\" class=\"data row3 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
              "      <td id=\"T_5df1f_row3_col3\" class=\"data row3 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_5df1f_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
              "      <td id=\"T_5df1f_row3_col5\" class=\"data row3 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row3_col6\" class=\"data row3 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
              "      <td id=\"T_5df1f_row4_col1\" class=\"data row4 col1\" >Training Test Degradation</td>\n",
              "      <td id=\"T_5df1f_row4_col2\" class=\"data row4 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
              "      <td id=\"T_5df1f_row4_col3\" class=\"data row4 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_5df1f_row4_col4\" class=\"data row4 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_5df1f_row4_col5\" class=\"data row4 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row4_col6\" class=\"data row4 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
              "      <td id=\"T_5df1f_row5_col1\" class=\"data row5 col1\" >Calibration Curve Drift</td>\n",
              "      <td id=\"T_5df1f_row5_col2\" class=\"data row5 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_5df1f_row5_col3\" class=\"data row5 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_5df1f_row5_col4\" class=\"data row5 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
              "      <td id=\"T_5df1f_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5df1f_row6_col0\" class=\"data row6 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
              "      <td id=\"T_5df1f_row6_col1\" class=\"data row6 col1\" >ROC Curve Drift</td>\n",
              "      <td id=\"T_5df1f_row6_col2\" class=\"data row6 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_5df1f_row6_col3\" class=\"data row6 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_5df1f_row6_col4\" class=\"data row6 col4\" >{}</td>\n",
              "      <td id=\"T_5df1f_row6_col5\" class=\"data row6 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_5df1f_row6_col6\" class=\"data row6 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x37563ceb0>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tests(tags=[\"model_performance\", \"visualization\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The above parameters can be combined to create complex queries. For instance, to find tests that are compatible with 'sklearn' models, designed for 'classification' tasks, and produce a plot:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6455a th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_6455a_row0_col0, #T_6455a_row0_col1, #T_6455a_row0_col2, #T_6455a_row0_col3, #T_6455a_row0_col4, #T_6455a_row0_col5, #T_6455a_row0_col6, #T_6455a_row1_col0, #T_6455a_row1_col1, #T_6455a_row1_col2, #T_6455a_row1_col3, #T_6455a_row1_col4, #T_6455a_row1_col5, #T_6455a_row1_col6, #T_6455a_row2_col0, #T_6455a_row2_col1, #T_6455a_row2_col2, #T_6455a_row2_col3, #T_6455a_row2_col4, #T_6455a_row2_col5, #T_6455a_row2_col6, #T_6455a_row3_col0, #T_6455a_row3_col1, #T_6455a_row3_col2, #T_6455a_row3_col3, #T_6455a_row3_col4, #T_6455a_row3_col5, #T_6455a_row3_col6, #T_6455a_row4_col0, #T_6455a_row4_col1, #T_6455a_row4_col2, #T_6455a_row4_col3, #T_6455a_row4_col4, #T_6455a_row4_col5, #T_6455a_row4_col6, #T_6455a_row5_col0, #T_6455a_row5_col1, #T_6455a_row5_col2, #T_6455a_row5_col3, #T_6455a_row5_col4, #T_6455a_row5_col5, #T_6455a_row5_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6455a\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_6455a_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
              "      <th id=\"T_6455a_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
              "      <th id=\"T_6455a_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
              "      <th id=\"T_6455a_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
              "      <th id=\"T_6455a_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
              "      <th id=\"T_6455a_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
              "      <th id=\"T_6455a_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
              "      <td id=\"T_6455a_row0_col1\" class=\"data row0 col1\" >Confusion Matrix</td>\n",
              "      <td id=\"T_6455a_row0_col2\" class=\"data row0 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
              "      <td id=\"T_6455a_row0_col3\" class=\"data row0 col3\" >['dataset', 'model']</td>\n",
              "      <td id=\"T_6455a_row0_col4\" class=\"data row0 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
              "      <td id=\"T_6455a_row0_col5\" class=\"data row0 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row0_col6\" class=\"data row0 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
              "      <td id=\"T_6455a_row1_col1\" class=\"data row1 col1\" >Precision Recall Curve</td>\n",
              "      <td id=\"T_6455a_row1_col2\" class=\"data row1 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
              "      <td id=\"T_6455a_row1_col3\" class=\"data row1 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_6455a_row1_col4\" class=\"data row1 col4\" >{}</td>\n",
              "      <td id=\"T_6455a_row1_col5\" class=\"data row1 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row1_col6\" class=\"data row1 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
              "      <td id=\"T_6455a_row2_col1\" class=\"data row2 col1\" >ROC Curve</td>\n",
              "      <td id=\"T_6455a_row2_col2\" class=\"data row2 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
              "      <td id=\"T_6455a_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
              "      <td id=\"T_6455a_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
              "      <td id=\"T_6455a_row2_col5\" class=\"data row2 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row2_col6\" class=\"data row2 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
              "      <td id=\"T_6455a_row3_col1\" class=\"data row3 col1\" >Training Test Degradation</td>\n",
              "      <td id=\"T_6455a_row3_col2\" class=\"data row3 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
              "      <td id=\"T_6455a_row3_col3\" class=\"data row3 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_6455a_row3_col4\" class=\"data row3 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
              "      <td id=\"T_6455a_row3_col5\" class=\"data row3 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row3_col6\" class=\"data row3 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row4_col0\" class=\"data row4 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
              "      <td id=\"T_6455a_row4_col1\" class=\"data row4 col1\" >Calibration Curve Drift</td>\n",
              "      <td id=\"T_6455a_row4_col2\" class=\"data row4 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_6455a_row4_col3\" class=\"data row4 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_6455a_row4_col4\" class=\"data row4 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
              "      <td id=\"T_6455a_row4_col5\" class=\"data row4 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row4_col6\" class=\"data row4 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_6455a_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
              "      <td id=\"T_6455a_row5_col1\" class=\"data row5 col1\" >ROC Curve Drift</td>\n",
              "      <td id=\"T_6455a_row5_col2\" class=\"data row5 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
              "      <td id=\"T_6455a_row5_col3\" class=\"data row5 col3\" >['datasets', 'model']</td>\n",
              "      <td id=\"T_6455a_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
              "      <td id=\"T_6455a_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
              "      <td id=\"T_6455a_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x375608640>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_tests(\n",
        "    tags=[\"model_performance\", \"visualization\", \"sklearn\"], task=\"classification\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Programmatic Use\n",
        "\n",
        "To work with a specific set of tests programmatically, you can store the results in a variable. For instance, let's list all tests that are designed for Text Summarization tests and store them in `text_summarization_tests` for further use.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['validmind.data_validation.DatasetDescription',\n",
              " 'validmind.data_validation.DatasetSplit',\n",
              " 'validmind.data_validation.nlp.CommonWords',\n",
              " 'validmind.data_validation.nlp.Hashtags',\n",
              " 'validmind.data_validation.nlp.LanguageDetection',\n",
              " 'validmind.data_validation.nlp.Mentions',\n",
              " 'validmind.data_validation.nlp.Punctuations',\n",
              " 'validmind.data_validation.nlp.StopWords',\n",
              " 'validmind.data_validation.nlp.TextDescription',\n",
              " 'validmind.model_validation.BertScore',\n",
              " 'validmind.model_validation.BleuScore',\n",
              " 'validmind.model_validation.ContextualRecall',\n",
              " 'validmind.model_validation.MeteorScore',\n",
              " 'validmind.model_validation.RegardScore',\n",
              " 'validmind.model_validation.RougeScore',\n",
              " 'validmind.model_validation.TokenDisparity',\n",
              " 'validmind.model_validation.ToxicityScore',\n",
              " 'validmind.model_validation.embeddings.CosineSimilarityComparison',\n",
              " 'validmind.model_validation.embeddings.CosineSimilarityHeatmap',\n",
              " 'validmind.model_validation.embeddings.EuclideanDistanceComparison',\n",
              " 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap',\n",
              " 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',\n",
              " 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',\n",
              " 'validmind.model_validation.ragas.AnswerCorrectness',\n",
              " 'validmind.model_validation.ragas.AspectCritic',\n",
              " 'validmind.model_validation.ragas.ContextEntityRecall',\n",
              " 'validmind.model_validation.ragas.ContextPrecision',\n",
              " 'validmind.model_validation.ragas.ContextPrecisionWithoutReference',\n",
              " 'validmind.model_validation.ragas.ContextRecall',\n",
              " 'validmind.model_validation.ragas.Faithfulness',\n",
              " 'validmind.model_validation.ragas.NoiseSensitivity',\n",
              " 'validmind.model_validation.ragas.ResponseRelevancy',\n",
              " 'validmind.model_validation.ragas.SemanticSimilarity',\n",
              " 'validmind.prompt_validation.Bias',\n",
              " 'validmind.prompt_validation.Clarity',\n",
              " 'validmind.prompt_validation.Conciseness',\n",
              " 'validmind.prompt_validation.Delimitation',\n",
              " 'validmind.prompt_validation.NegativeInstruction',\n",
              " 'validmind.prompt_validation.Robustness',\n",
              " 'validmind.prompt_validation.Specificity']"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_summarization_tests = list_tests(task=\"text_summarization\", pretty=False)\n",
        "text_summarization_tests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id='toc4_'></a>\n",
        "\n",
        "## Delving into Test Details with `describe_test`\n",
        "\n",
        "After identifying a set of potential tests, you might want to explore the specifics of an individual test. The `describe_test` function provides a deep dive into the details of a test. It reveals the test name, description, ID, test type, and required inputs. Below, we showcase how to describe a test using its ID:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5025f3a7dbb34f4c9de1b26e4909f3f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Accordion(children=(HTML(value='\\n<div>\\n  <h2>Overfit Diagnosis</h2>\\n  <div style=\"border: 1px solid #ddd; b…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "<script defer type=\"module\">\n",
              "import hljs from 'https://unpkg.com/@highlightjs/cdn-assets@11.9.0/es/highlight.min.js';\n",
              "import python from 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/es/languages/python.min.js';\n",
              "\n",
              "hljs.registerLanguage('python', python);\n",
              "hljs.highlightAll();\n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "<script>\n",
              "window.MathJax = {\n",
              "    tex2jax: {\n",
              "        inlineMath: [['$', '$'], ['\\\\(', '\\\\)']],\n",
              "        displayMath: [['$$', '$$'], ['\\[', '\\]']],\n",
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              "        skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'],\n",
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              "        processClass: \"math\"\n",
              "    }\n",
              "};\n",
              "setTimeout(function () {\n",
              "    var script = document.createElement('script');\n",
              "    script.type = 'text/javascript';\n",
              "    script.src = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-AMS_HTML';\n",
              "    document.head.appendChild(script);\n",
              "}, 300);\n",
              "</script>\n"
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              "<IPython.core.display.HTML object>"
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "describe_test(\"validmind.model_validation.sklearn.OverfitDiagnosis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a id='toc5_'></a>\n",
        "\n",
        "## Next steps\n",
        "\n",
        "By harnessing the functionalities presented in this guide, you should be able to easily list and filter through all of ValidMind's available tests and find those you are interested in running against your model and/or dataset. The next step is to take the IDs of the tests you'd like to run and either create a test suite for reuse or just run them directly to try them out. See the other notebooks for a tutorial on how to do both.\n",
        "\n",
        "<a id='toc5_1_'></a>\n",
        "\n",
        "### Discover more learning resources\n",
        "\n",
        "We offer many interactive notebooks to help you document models:\n",
        "\n",
        "- [Run tests & test suites](https://docs.validmind.ai/developer/model-testing/testing-overview.html)\n",
        "- [Code samples](https://docs.validmind.ai/developer/samples-jupyter-notebooks.html)\n",
        "\n",
        "Or, visit our [documentation](https://docs.validmind.ai/) to learn more about ValidMind.\n"
      ]
    }
  ],
  "metadata": {
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      "display_name": ".venv",
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