{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore test suites\n",
    "\n",
    "Explore the the test suites and tests that are available in the ValidMind Library, and retrieve detailed information for each.\n",
    "\n",
    "This notebook shows how you can learn more about the test suites and tests that are available in the ValidMind Library. The step-by-step instructions provide all the code required to retrieve a list of available test suites, details for a specific test suite, details for a specific test within a suite, a verbose details view of a test suite and its tests, and a list of all tests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "- [About ValidMind](#toc1_)    \n",
    "  - [Before you begin](#toc1_1_)    \n",
    "  - [New to ValidMind?](#toc1_2_)    \n",
    "  - [Key concepts](#toc1_3_)    \n",
    "- [Install the ValidMind Library](#toc2_)    \n",
    "- [Get a list of available test suites](#toc3_)    \n",
    "- [Get details for a test suite](#toc4_)    \n",
    "  - [Get details for a test](#toc4_1_)    \n",
    "  - [Get a verbose details view of a test suite and its tests](#toc4_2_)    \n",
    "- [List all tests](#toc5_)    \n",
    "- [Next steps](#toc6_)    \n",
    "  - [Discover more learning resources](#toc6_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 -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc1_'></a>\n",
    "\n",
    "## About ValidMind\n",
    "\n",
    "ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.\n",
    "\n",
    "You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.\n",
    "\n",
    "<a id='toc1_1_'></a>\n",
    "\n",
    "### Before you begin\n",
    "\n",
    "This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language. \n",
    "\n",
    "If you encounter errors due to missing modules in your Python environment, install the modules with `pip install`, and then re-run the notebook. For more help, refer to [Installing Python Modules](https://docs.python.org/3/installing/index.html).\n",
    "\n",
    "<a id='toc1_2_'></a>\n",
    "\n",
    "### New to ValidMind?\n",
    "\n",
    "If you haven't already seen our documentation on the [ValidMind Library](https://docs.validmind.ai/developer/validmind-library.html), we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"background-color: #B5B5B510; color: black; border: 1px solid #083E44; border-left-width: 5px; box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);border-radius: 5px;\"><span style=\"color: #083E44;\"><b>For access to all features available in this notebook, create a free ValidMind account.</b></span>\n",
    "<br></br>\n",
    "Signing up is FREE — <a href=\"https://docs.validmind.ai/guide/configuration/register-with-validmind.html\" style=\"color: #DE257E;\"><b>Register with ValidMind</b></a></div>\n",
    "\n",
    "<a id='toc1_3_'></a>\n",
    "\n",
    "### Key concepts\n",
    "\n",
    "**Model documentation**: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.\n",
    "\n",
    "**Documentation template**: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.\n",
    "\n",
    "**Tests**: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.\n",
    "\n",
    "**Custom tests**: Custom tests are functions that you define to evaluate your model or dataset. These functions can be registered via the ValidMind Library to be used with the ValidMind Platform.\n",
    "\n",
    "**Inputs**: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:\n",
    "\n",
    "  - **model**: A single model that has been initialized in ValidMind with [`vm.init_model()`](https://docs.validmind.ai/validmind/validmind.html#init_model).\n",
    "  - **dataset**: Single dataset that has been initialized in ValidMind with [`vm.init_dataset()`](https://docs.validmind.ai/validmind/validmind.html#init_dataset).\n",
    "  - **models**: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom test.\n",
    "  - **datasets**: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom test. See this [example](https://docs.validmind.ai/notebooks/how_to/run_tests_that_require_multiple_datasets.html) for more information.\n",
    "\n",
    "**Parameters**: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.\n",
    "\n",
    "**Outputs**: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.\n",
    "\n",
    "**Test suites**: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.\n",
    "\n",
    "Example: the [`classifier_full_suite`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html#ClassifierFullSuite) test suite runs tests from the [`tabular_dataset`](https://docs.validmind.ai/validmind/validmind/test_suites/tabular_datasets.html) and [`classifier`](https://docs.validmind.ai/validmind/validmind/test_suites/classifier.html) test suites to fully document the data and model sections for binary classification model use-cases.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc2_'></a>\n",
    "\n",
    "## Install the ValidMind Library\n",
    "\n",
    "To install the library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 24.0 is available.\n",
      "You should consider upgrading via the '/Users/andres/code/validmind-sdk/.venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q validmind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc3_'></a>\n",
    "\n",
    "## Get a list of available test suites\n",
    "\n",
    "To get the list of all test suites available in the ValidMind Library:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_11673 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_11673_row0_col0, #T_11673_row0_col1, #T_11673_row0_col2, #T_11673_row0_col3, #T_11673_row1_col0, #T_11673_row1_col1, #T_11673_row1_col2, #T_11673_row1_col3, #T_11673_row2_col0, #T_11673_row2_col1, #T_11673_row2_col2, #T_11673_row2_col3, #T_11673_row3_col0, #T_11673_row3_col1, #T_11673_row3_col2, #T_11673_row3_col3, #T_11673_row4_col0, #T_11673_row4_col1, #T_11673_row4_col2, #T_11673_row4_col3, #T_11673_row5_col0, #T_11673_row5_col1, #T_11673_row5_col2, #T_11673_row5_col3, #T_11673_row6_col0, #T_11673_row6_col1, #T_11673_row6_col2, #T_11673_row6_col3, #T_11673_row7_col0, #T_11673_row7_col1, #T_11673_row7_col2, #T_11673_row7_col3, #T_11673_row8_col0, #T_11673_row8_col1, #T_11673_row8_col2, #T_11673_row8_col3, #T_11673_row9_col0, #T_11673_row9_col1, #T_11673_row9_col2, #T_11673_row9_col3, #T_11673_row10_col0, #T_11673_row10_col1, #T_11673_row10_col2, #T_11673_row10_col3, #T_11673_row11_col0, #T_11673_row11_col1, #T_11673_row11_col2, #T_11673_row11_col3, #T_11673_row12_col0, #T_11673_row12_col1, #T_11673_row12_col2, #T_11673_row12_col3, #T_11673_row13_col0, #T_11673_row13_col1, #T_11673_row13_col2, #T_11673_row13_col3, #T_11673_row14_col0, #T_11673_row14_col1, #T_11673_row14_col2, #T_11673_row14_col3, #T_11673_row15_col0, #T_11673_row15_col1, #T_11673_row15_col2, #T_11673_row15_col3, #T_11673_row16_col0, #T_11673_row16_col1, #T_11673_row16_col2, #T_11673_row16_col3, #T_11673_row17_col0, #T_11673_row17_col1, #T_11673_row17_col2, #T_11673_row17_col3, #T_11673_row18_col0, #T_11673_row18_col1, #T_11673_row18_col2, #T_11673_row18_col3, #T_11673_row19_col0, #T_11673_row19_col1, #T_11673_row19_col2, #T_11673_row19_col3, #T_11673_row20_col0, #T_11673_row20_col1, #T_11673_row20_col2, #T_11673_row20_col3, #T_11673_row21_col0, #T_11673_row21_col1, #T_11673_row21_col2, #T_11673_row21_col3, #T_11673_row22_col0, #T_11673_row22_col1, #T_11673_row22_col2, #T_11673_row22_col3, #T_11673_row23_col0, #T_11673_row23_col1, #T_11673_row23_col2, #T_11673_row23_col3, #T_11673_row24_col0, #T_11673_row24_col1, #T_11673_row24_col2, #T_11673_row24_col3, #T_11673_row25_col0, #T_11673_row25_col1, #T_11673_row25_col2, #T_11673_row25_col3, #T_11673_row26_col0, #T_11673_row26_col1, #T_11673_row26_col2, #T_11673_row26_col3, #T_11673_row27_col0, #T_11673_row27_col1, #T_11673_row27_col2, #T_11673_row27_col3, #T_11673_row28_col0, #T_11673_row28_col1, #T_11673_row28_col2, #T_11673_row28_col3, #T_11673_row29_col0, #T_11673_row29_col1, #T_11673_row29_col2, #T_11673_row29_col3, #T_11673_row30_col0, #T_11673_row30_col1, #T_11673_row30_col2, #T_11673_row30_col3 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_11673\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_11673_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th id=\"T_11673_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_11673_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_11673_level0_col3\" class=\"col_heading level0 col3\" >Tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row0_col0\" class=\"data row0 col0\" >classifier_model_diagnosis</td>\n",
       "      <td id=\"T_11673_row0_col1\" class=\"data row0 col1\" >ClassifierDiagnosis</td>\n",
       "      <td id=\"T_11673_row0_col2\" class=\"data row0 col2\" >Test suite for sklearn classifier model diagnosis tests</td>\n",
       "      <td id=\"T_11673_row0_col3\" class=\"data row0 col3\" >validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row1_col0\" class=\"data row1 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_11673_row1_col1\" class=\"data row1 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_11673_row1_col2\" class=\"data row1 col2\" >Full test suite for binary classification models.</td>\n",
       "      <td id=\"T_11673_row1_col3\" class=\"data row1 col3\" >validmind.data_validation.DatasetDescription, validmind.data_validation.DescriptiveStatistics, validmind.data_validation.PearsonCorrelationMatrix, validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.HighCardinality, validmind.data_validation.HighPearsonCorrelation, validmind.data_validation.MissingValues, validmind.data_validation.Skewness, validmind.data_validation.UniqueRows, validmind.data_validation.TooManyZeroValues, validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance, validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison, validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row2_col0\" class=\"data row2 col0\" >classifier_metrics</td>\n",
       "      <td id=\"T_11673_row2_col1\" class=\"data row2 col1\" >ClassifierMetrics</td>\n",
       "      <td id=\"T_11673_row2_col2\" class=\"data row2 col2\" >Test suite for sklearn classifier metrics</td>\n",
       "      <td id=\"T_11673_row2_col3\" class=\"data row2 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row3_col0\" class=\"data row3 col0\" >classifier_model_validation</td>\n",
       "      <td id=\"T_11673_row3_col1\" class=\"data row3 col1\" >ClassifierModelValidation</td>\n",
       "      <td id=\"T_11673_row3_col2\" class=\"data row3 col2\" >Test suite for binary classification models.</td>\n",
       "      <td id=\"T_11673_row3_col3\" class=\"data row3 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance, validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison, validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row4_col0\" class=\"data row4 col0\" >classifier_validation</td>\n",
       "      <td id=\"T_11673_row4_col1\" class=\"data row4 col1\" >ClassifierPerformance</td>\n",
       "      <td id=\"T_11673_row4_col2\" class=\"data row4 col2\" >Test suite for sklearn classifier models</td>\n",
       "      <td id=\"T_11673_row4_col3\" class=\"data row4 col3\" >validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row5_col0\" class=\"data row5 col0\" >cluster_full_suite</td>\n",
       "      <td id=\"T_11673_row5_col1\" class=\"data row5 col1\" >ClusterFullSuite</td>\n",
       "      <td id=\"T_11673_row5_col2\" class=\"data row5 col2\" >Full test suite for clustering models.</td>\n",
       "      <td id=\"T_11673_row5_col3\" class=\"data row5 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.HomogeneityScore, validmind.model_validation.sklearn.CompletenessScore, validmind.model_validation.sklearn.VMeasure, validmind.model_validation.sklearn.AdjustedRandIndex, validmind.model_validation.sklearn.AdjustedMutualInformation, validmind.model_validation.sklearn.FowlkesMallowsScore, validmind.model_validation.sklearn.ClusterPerformanceMetrics, validmind.model_validation.sklearn.ClusterCosineSimilarity, validmind.model_validation.sklearn.SilhouettePlot, validmind.model_validation.ClusterSizeDistribution, validmind.model_validation.sklearn.HyperParametersTuning, validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row6_col0\" class=\"data row6 col0\" >cluster_metrics</td>\n",
       "      <td id=\"T_11673_row6_col1\" class=\"data row6 col1\" >ClusterMetrics</td>\n",
       "      <td id=\"T_11673_row6_col2\" class=\"data row6 col2\" >Test suite for sklearn clustering metrics</td>\n",
       "      <td id=\"T_11673_row6_col3\" class=\"data row6 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.HomogeneityScore, validmind.model_validation.sklearn.CompletenessScore, validmind.model_validation.sklearn.VMeasure, validmind.model_validation.sklearn.AdjustedRandIndex, validmind.model_validation.sklearn.AdjustedMutualInformation, validmind.model_validation.sklearn.FowlkesMallowsScore, validmind.model_validation.sklearn.ClusterPerformanceMetrics, validmind.model_validation.sklearn.ClusterCosineSimilarity, validmind.model_validation.sklearn.SilhouettePlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row7_col0\" class=\"data row7 col0\" >cluster_performance</td>\n",
       "      <td id=\"T_11673_row7_col1\" class=\"data row7 col1\" >ClusterPerformance</td>\n",
       "      <td id=\"T_11673_row7_col2\" class=\"data row7 col2\" >Test suite for sklearn cluster performance</td>\n",
       "      <td id=\"T_11673_row7_col3\" class=\"data row7 col3\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row8_col0\" class=\"data row8 col0\" >embeddings_full_suite</td>\n",
       "      <td id=\"T_11673_row8_col1\" class=\"data row8 col1\" >EmbeddingsFullSuite</td>\n",
       "      <td id=\"T_11673_row8_col2\" class=\"data row8 col2\" >Full test suite for embeddings models.</td>\n",
       "      <td id=\"T_11673_row8_col3\" class=\"data row8 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.embeddings.DescriptiveAnalytics, validmind.model_validation.embeddings.CosineSimilarityDistribution, validmind.model_validation.embeddings.ClusterDistribution, validmind.model_validation.embeddings.EmbeddingsVisualization2D, validmind.model_validation.embeddings.StabilityAnalysisRandomNoise, validmind.model_validation.embeddings.StabilityAnalysisSynonyms, validmind.model_validation.embeddings.StabilityAnalysisKeyword, validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row9_col0\" class=\"data row9 col0\" >embeddings_metrics</td>\n",
       "      <td id=\"T_11673_row9_col1\" class=\"data row9 col1\" >EmbeddingsMetrics</td>\n",
       "      <td id=\"T_11673_row9_col2\" class=\"data row9 col2\" >Test suite for embeddings metrics</td>\n",
       "      <td id=\"T_11673_row9_col3\" class=\"data row9 col3\" >validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.embeddings.DescriptiveAnalytics, validmind.model_validation.embeddings.CosineSimilarityDistribution, validmind.model_validation.embeddings.ClusterDistribution, validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row10_col0\" class=\"data row10 col0\" >embeddings_model_performance</td>\n",
       "      <td id=\"T_11673_row10_col1\" class=\"data row10 col1\" >EmbeddingsPerformance</td>\n",
       "      <td id=\"T_11673_row10_col2\" class=\"data row10 col2\" >Test suite for embeddings model performance</td>\n",
       "      <td id=\"T_11673_row10_col3\" class=\"data row10 col3\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise, validmind.model_validation.embeddings.StabilityAnalysisSynonyms, validmind.model_validation.embeddings.StabilityAnalysisKeyword, validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row11_col0\" class=\"data row11 col0\" >hyper_parameters_optimization</td>\n",
       "      <td id=\"T_11673_row11_col1\" class=\"data row11 col1\" >KmeansParametersOptimization</td>\n",
       "      <td id=\"T_11673_row11_col2\" class=\"data row11 col2\" >Test suite for sklearn hyperparameters optimization</td>\n",
       "      <td id=\"T_11673_row11_col3\" class=\"data row11 col3\" >validmind.model_validation.sklearn.HyperParametersTuning, validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row12_col0\" class=\"data row12 col0\" >llm_classifier_full_suite</td>\n",
       "      <td id=\"T_11673_row12_col1\" class=\"data row12 col1\" >LLMClassifierFullSuite</td>\n",
       "      <td id=\"T_11673_row12_col2\" class=\"data row12 col2\" >Full test suite for LLM classification models.</td>\n",
       "      <td id=\"T_11673_row12_col3\" class=\"data row12 col3\" >validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.nlp.StopWords, validmind.data_validation.nlp.Punctuations, validmind.data_validation.nlp.CommonWords, validmind.data_validation.nlp.TextDescription, validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance, validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison, validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis, validmind.prompt_validation.Bias, validmind.prompt_validation.Clarity, validmind.prompt_validation.Conciseness, validmind.prompt_validation.Delimitation, validmind.prompt_validation.NegativeInstruction, validmind.prompt_validation.Robustness, validmind.prompt_validation.Specificity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row13_col0\" class=\"data row13 col0\" >prompt_validation</td>\n",
       "      <td id=\"T_11673_row13_col1\" class=\"data row13 col1\" >PromptValidation</td>\n",
       "      <td id=\"T_11673_row13_col2\" class=\"data row13 col2\" >Test suite for prompt validation</td>\n",
       "      <td id=\"T_11673_row13_col3\" class=\"data row13 col3\" >validmind.prompt_validation.Bias, validmind.prompt_validation.Clarity, validmind.prompt_validation.Conciseness, validmind.prompt_validation.Delimitation, validmind.prompt_validation.NegativeInstruction, validmind.prompt_validation.Robustness, validmind.prompt_validation.Specificity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row14_col0\" class=\"data row14 col0\" >nlp_classifier_full_suite</td>\n",
       "      <td id=\"T_11673_row14_col1\" class=\"data row14 col1\" >NLPClassifierFullSuite</td>\n",
       "      <td id=\"T_11673_row14_col2\" class=\"data row14 col2\" >Full test suite for NLP classification models.</td>\n",
       "      <td id=\"T_11673_row14_col3\" class=\"data row14 col3\" >validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.nlp.StopWords, validmind.data_validation.nlp.Punctuations, validmind.data_validation.nlp.CommonWords, validmind.data_validation.nlp.TextDescription, validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance, validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison, validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row15_col0\" class=\"data row15 col0\" >regression_metrics</td>\n",
       "      <td id=\"T_11673_row15_col1\" class=\"data row15 col1\" >RegressionMetrics</td>\n",
       "      <td id=\"T_11673_row15_col2\" class=\"data row15 col2\" >Test suite for performance metrics of regression metrics</td>\n",
       "      <td id=\"T_11673_row15_col3\" class=\"data row15 col3\" >validmind.data_validation.DatasetSplit, validmind.model_validation.ModelMetadata, validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row16_col0\" class=\"data row16 col0\" >regression_model_description</td>\n",
       "      <td id=\"T_11673_row16_col1\" class=\"data row16 col1\" >RegressionModelDescription</td>\n",
       "      <td id=\"T_11673_row16_col2\" class=\"data row16 col2\" >Test suite for performance metric of regression model of statsmodels library</td>\n",
       "      <td id=\"T_11673_row16_col3\" class=\"data row16 col3\" >validmind.data_validation.DatasetSplit, validmind.model_validation.ModelMetadata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row17_col0\" class=\"data row17 col0\" >regression_models_evaluation</td>\n",
       "      <td id=\"T_11673_row17_col1\" class=\"data row17 col1\" >RegressionModelsEvaluation</td>\n",
       "      <td id=\"T_11673_row17_col2\" class=\"data row17 col2\" >Test suite for metrics comparison of regression model of statsmodels library</td>\n",
       "      <td id=\"T_11673_row17_col3\" class=\"data row17 col3\" >validmind.model_validation.statsmodels.RegressionModelsCoeffs, validmind.model_validation.sklearn.RegressionModelsPerformanceComparison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row18_col0\" class=\"data row18 col0\" >regression_full_suite</td>\n",
       "      <td id=\"T_11673_row18_col1\" class=\"data row18 col1\" >RegressionFullSuite</td>\n",
       "      <td id=\"T_11673_row18_col2\" class=\"data row18 col2\" >Full test suite for regression models.</td>\n",
       "      <td id=\"T_11673_row18_col3\" class=\"data row18 col3\" >validmind.data_validation.DatasetDescription, validmind.data_validation.DescriptiveStatistics, validmind.data_validation.PearsonCorrelationMatrix, validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.HighCardinality, validmind.data_validation.HighPearsonCorrelation, validmind.data_validation.MissingValues, validmind.data_validation.Skewness, validmind.data_validation.UniqueRows, validmind.data_validation.TooManyZeroValues, validmind.data_validation.DatasetSplit, validmind.model_validation.ModelMetadata, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.RegressionErrors, validmind.model_validation.sklearn.RegressionR2Square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row19_col0\" class=\"data row19 col0\" >regression_performance</td>\n",
       "      <td id=\"T_11673_row19_col1\" class=\"data row19 col1\" >RegressionPerformance</td>\n",
       "      <td id=\"T_11673_row19_col2\" class=\"data row19 col2\" >Test suite for regression model performance</td>\n",
       "      <td id=\"T_11673_row19_col3\" class=\"data row19 col3\" >validmind.model_validation.sklearn.RegressionErrors, validmind.model_validation.sklearn.RegressionR2Square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row20_col0\" class=\"data row20 col0\" >summarization_metrics</td>\n",
       "      <td id=\"T_11673_row20_col1\" class=\"data row20 col1\" >SummarizationMetrics</td>\n",
       "      <td id=\"T_11673_row20_col2\" class=\"data row20 col2\" >Test suite for Summarization metrics</td>\n",
       "      <td id=\"T_11673_row20_col3\" class=\"data row20 col3\" >validmind.model_validation.RougeMetrics, validmind.model_validation.TokenDisparity, validmind.model_validation.BleuScore, validmind.model_validation.BertScore, validmind.model_validation.ContextualRecall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row21_col0\" class=\"data row21 col0\" >tabular_dataset</td>\n",
       "      <td id=\"T_11673_row21_col1\" class=\"data row21 col1\" >TabularDataset</td>\n",
       "      <td id=\"T_11673_row21_col2\" class=\"data row21 col2\" >Test suite for tabular datasets.</td>\n",
       "      <td id=\"T_11673_row21_col3\" class=\"data row21 col3\" >validmind.data_validation.DatasetDescription, validmind.data_validation.DescriptiveStatistics, validmind.data_validation.PearsonCorrelationMatrix, validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.HighCardinality, validmind.data_validation.HighPearsonCorrelation, validmind.data_validation.MissingValues, validmind.data_validation.Skewness, validmind.data_validation.UniqueRows, validmind.data_validation.TooManyZeroValues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row22_col0\" class=\"data row22 col0\" >tabular_dataset_description</td>\n",
       "      <td id=\"T_11673_row22_col1\" class=\"data row22 col1\" >TabularDatasetDescription</td>\n",
       "      <td id=\"T_11673_row22_col2\" class=\"data row22 col2\" >Test suite to extract metadata and descriptive\n",
       "statistics from a tabular dataset</td>\n",
       "      <td id=\"T_11673_row22_col3\" class=\"data row22 col3\" >validmind.data_validation.DatasetDescription, validmind.data_validation.DescriptiveStatistics, validmind.data_validation.PearsonCorrelationMatrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row23_col0\" class=\"data row23 col0\" >tabular_data_quality</td>\n",
       "      <td id=\"T_11673_row23_col1\" class=\"data row23 col1\" >TabularDataQuality</td>\n",
       "      <td id=\"T_11673_row23_col2\" class=\"data row23 col2\" >Test suite for data quality on tabular datasets</td>\n",
       "      <td id=\"T_11673_row23_col3\" class=\"data row23 col3\" >validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.HighCardinality, validmind.data_validation.HighPearsonCorrelation, validmind.data_validation.MissingValues, validmind.data_validation.Skewness, validmind.data_validation.UniqueRows, validmind.data_validation.TooManyZeroValues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row24_col0\" class=\"data row24 col0\" >text_data_quality</td>\n",
       "      <td id=\"T_11673_row24_col1\" class=\"data row24 col1\" >TextDataQuality</td>\n",
       "      <td id=\"T_11673_row24_col2\" class=\"data row24 col2\" >Test suite for data quality on text data</td>\n",
       "      <td id=\"T_11673_row24_col3\" class=\"data row24 col3\" >validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.nlp.StopWords, validmind.data_validation.nlp.Punctuations, validmind.data_validation.nlp.CommonWords, validmind.data_validation.nlp.TextDescription</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row25_col0\" class=\"data row25 col0\" >time_series_data_quality</td>\n",
       "      <td id=\"T_11673_row25_col1\" class=\"data row25 col1\" >TimeSeriesDataQuality</td>\n",
       "      <td id=\"T_11673_row25_col2\" class=\"data row25 col2\" >Test suite for data quality on time series datasets</td>\n",
       "      <td id=\"T_11673_row25_col3\" class=\"data row25 col3\" >validmind.data_validation.TimeSeriesOutliers, validmind.data_validation.TimeSeriesMissingValues, validmind.data_validation.TimeSeriesFrequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row26_col0\" class=\"data row26 col0\" >time_series_dataset</td>\n",
       "      <td id=\"T_11673_row26_col1\" class=\"data row26 col1\" >TimeSeriesDataset</td>\n",
       "      <td id=\"T_11673_row26_col2\" class=\"data row26 col2\" >Test suite for time series datasets.</td>\n",
       "      <td id=\"T_11673_row26_col3\" class=\"data row26 col3\" >validmind.data_validation.TimeSeriesOutliers, validmind.data_validation.TimeSeriesMissingValues, validmind.data_validation.TimeSeriesFrequency, validmind.data_validation.TimeSeriesLinePlot, validmind.data_validation.TimeSeriesHistogram, validmind.data_validation.ACFandPACFPlot, validmind.data_validation.SeasonalDecompose, validmind.data_validation.AutoSeasonality, validmind.data_validation.AutoStationarity, validmind.data_validation.RollingStatsPlot, validmind.data_validation.AutoAR, validmind.data_validation.AutoMA, validmind.data_validation.ScatterPlot, validmind.data_validation.LaggedCorrelationHeatmap, validmind.data_validation.EngleGrangerCoint, validmind.data_validation.SpreadPlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row27_col0\" class=\"data row27 col0\" >time_series_model_validation</td>\n",
       "      <td id=\"T_11673_row27_col1\" class=\"data row27 col1\" >TimeSeriesModelValidation</td>\n",
       "      <td id=\"T_11673_row27_col2\" class=\"data row27 col2\" >Test suite for time series model validation.</td>\n",
       "      <td id=\"T_11673_row27_col3\" class=\"data row27 col3\" >validmind.data_validation.DatasetSplit, validmind.model_validation.ModelMetadata, validmind.model_validation.statsmodels.RegressionModelsCoeffs, validmind.model_validation.sklearn.RegressionModelsPerformanceComparison, validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels, validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row28_col0\" class=\"data row28 col0\" >time_series_multivariate</td>\n",
       "      <td id=\"T_11673_row28_col1\" class=\"data row28 col1\" >TimeSeriesMultivariate</td>\n",
       "      <td id=\"T_11673_row28_col2\" class=\"data row28 col2\" >This test suite provides a preliminary understanding of the features\n",
       "and relationship in multivariate dataset. It presents various\n",
       "multivariate visualizations that can help identify patterns, trends,\n",
       "and relationships between pairs of variables. The visualizations are\n",
       "designed to explore the relationships between multiple features\n",
       "simultaneously. They allow you to quickly identify any patterns or\n",
       "trends in the data, as well as any potential outliers or anomalies.\n",
       "The individual feature distribution can also be explored to provide\n",
       "insight into the range and frequency of values observed in the data.\n",
       "This multivariate analysis test suite aims to provide an overview of\n",
       "the data structure and guide further exploration and modeling.</td>\n",
       "      <td id=\"T_11673_row28_col3\" class=\"data row28 col3\" >validmind.data_validation.ScatterPlot, validmind.data_validation.LaggedCorrelationHeatmap, validmind.data_validation.EngleGrangerCoint, validmind.data_validation.SpreadPlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row29_col0\" class=\"data row29 col0\" >time_series_sensitivity</td>\n",
       "      <td id=\"T_11673_row29_col1\" class=\"data row29 col1\" >TimeSeriesSensitivity</td>\n",
       "      <td id=\"T_11673_row29_col2\" class=\"data row29 col2\" >This test suite performs sensitivity analysis on a statsmodels OLS linear regression model\n",
       "by applying distinct shocks to each input variable individually and then computing the\n",
       "model's predictions. The aim of this test suite is to investigate the model's responsiveness\n",
       "to variations in its inputs. By juxtaposing the model's predictions under baseline and shocked\n",
       "conditions, users can visually evaluate the sensitivity of the model to changes in each\n",
       "variable. This kind of analysis can also shed light on potential model limitations, including\n",
       "over-reliance on specific variables or insufficient responsiveness to changes in inputs. As a\n",
       "result, this test suite can provide insights that may be beneficial for refining the model\n",
       "structure, improving its robustness, and ensuring a more reliable prediction performance.</td>\n",
       "      <td id=\"T_11673_row29_col3\" class=\"data row29 col3\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_11673_row30_col0\" class=\"data row30 col0\" >time_series_univariate</td>\n",
       "      <td id=\"T_11673_row30_col1\" class=\"data row30 col1\" >TimeSeriesUnivariate</td>\n",
       "      <td id=\"T_11673_row30_col2\" class=\"data row30 col2\" >This test suite provides a preliminary understanding of the target variable(s)\n",
       "used in the time series dataset. It visualizations that present the raw time\n",
       "series data and a histogram of the target variable(s).\n",
       "\n",
       "The raw time series data provides a visual inspection of the target variable's\n",
       "behavior over time. This helps to identify any patterns or trends in the data,\n",
       "as well as any potential outliers or anomalies. The histogram of the target\n",
       "variable displays the distribution of values, providing insight into the range\n",
       "and frequency of values observed in the data.</td>\n",
       "      <td id=\"T_11673_row30_col3\" class=\"data row30 col3\" >validmind.data_validation.TimeSeriesLinePlot, validmind.data_validation.TimeSeriesHistogram, validmind.data_validation.ACFandPACFPlot, validmind.data_validation.SeasonalDecompose, validmind.data_validation.AutoSeasonality, validmind.data_validation.AutoStationarity, validmind.data_validation.RollingStatsPlot, validmind.data_validation.AutoAR, validmind.data_validation.AutoMA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x112c308e0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import validmind as vm\n",
    "\n",
    "vm.test_suites.list_suites()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc4_'></a>\n",
    "\n",
    "## Get details for a test suite\n",
    "\n",
    "To get the list of tests available in a given test suite:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_27a31 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_27a31_row0_col0, #T_27a31_row0_col1, #T_27a31_row0_col2, #T_27a31_row0_col3 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_27a31\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_27a31_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th id=\"T_27a31_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_27a31_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_27a31_level0_col3\" class=\"col_heading level0 col3\" >Tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_27a31_row0_col0\" class=\"data row0 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_27a31_row0_col1\" class=\"data row0 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_27a31_row0_col2\" class=\"data row0 col2\" >Full test suite for binary classification models.</td>\n",
       "      <td id=\"T_27a31_row0_col3\" class=\"data row0 col3\" >validmind.data_validation.DatasetDescription, validmind.data_validation.DescriptiveStatistics, validmind.data_validation.PearsonCorrelationMatrix, validmind.data_validation.ClassImbalance, validmind.data_validation.Duplicates, validmind.data_validation.HighCardinality, validmind.data_validation.HighPearsonCorrelation, validmind.data_validation.MissingValues, validmind.data_validation.Skewness, validmind.data_validation.UniqueRows, validmind.data_validation.TooManyZeroValues, validmind.model_validation.ModelMetadata, validmind.data_validation.DatasetSplit, validmind.model_validation.sklearn.ConfusionMatrix, validmind.model_validation.sklearn.ClassifierPerformance, validmind.model_validation.sklearn.PermutationFeatureImportance, validmind.model_validation.sklearn.PrecisionRecallCurve, validmind.model_validation.sklearn.ROCCurve, validmind.model_validation.sklearn.PopulationStabilityIndex, validmind.model_validation.sklearn.SHAPGlobalImportance, validmind.model_validation.sklearn.MinimumAccuracy, validmind.model_validation.sklearn.MinimumF1Score, validmind.model_validation.sklearn.MinimumROCAUCScore, validmind.model_validation.sklearn.TrainingTestDegradation, validmind.model_validation.sklearn.ModelsPerformanceComparison, validmind.model_validation.sklearn.OverfitDiagnosis, validmind.model_validation.sklearn.WeakspotsDiagnosis, validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x30feb8c40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vm.test_suites.describe_suite(\"classifier_full_suite\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc4_1_'></a>\n",
    "\n",
    "### Get details for a test\n",
    "\n",
    "To get the details for a given test:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d9a73d8fabb4f2babca8a1a4bc4e31a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='\\n<div>\\n  <h2>Descriptive Statistics</h2>\\n  <p>Performs a detailed descripti…"
      ]
     },
     "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",
       "        processEscapes: true,\n",
       "        skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'],\n",
       "        ignoreClass: \".*\",\n",
       "        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"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vm.tests.describe_test(\"validmind.data_validation.DescriptiveStatistics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc4_2_'></a>\n",
    "\n",
    "### Get a verbose details view of a test suite and its tests\n",
    "\n",
    "To get more comprehensive details for test suites and tests:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3a730 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_3a730_row0_col0, #T_3a730_row0_col1, #T_3a730_row0_col2, #T_3a730_row0_col3, #T_3a730_row0_col4, #T_3a730_row1_col0, #T_3a730_row1_col1, #T_3a730_row1_col2, #T_3a730_row1_col3, #T_3a730_row1_col4, #T_3a730_row2_col0, #T_3a730_row2_col1, #T_3a730_row2_col2, #T_3a730_row2_col3, #T_3a730_row2_col4, #T_3a730_row3_col0, #T_3a730_row3_col1, #T_3a730_row3_col2, #T_3a730_row3_col3, #T_3a730_row3_col4, #T_3a730_row4_col0, #T_3a730_row4_col1, #T_3a730_row4_col2, #T_3a730_row4_col3, #T_3a730_row4_col4, #T_3a730_row5_col0, #T_3a730_row5_col1, #T_3a730_row5_col2, #T_3a730_row5_col3, #T_3a730_row5_col4, #T_3a730_row6_col0, #T_3a730_row6_col1, #T_3a730_row6_col2, #T_3a730_row6_col3, #T_3a730_row6_col4, #T_3a730_row7_col0, #T_3a730_row7_col1, #T_3a730_row7_col2, #T_3a730_row7_col3, #T_3a730_row7_col4, #T_3a730_row8_col0, #T_3a730_row8_col1, #T_3a730_row8_col2, #T_3a730_row8_col3, #T_3a730_row8_col4, #T_3a730_row9_col0, #T_3a730_row9_col1, #T_3a730_row9_col2, #T_3a730_row9_col3, #T_3a730_row9_col4, #T_3a730_row10_col0, #T_3a730_row10_col1, #T_3a730_row10_col2, #T_3a730_row10_col3, #T_3a730_row10_col4, #T_3a730_row11_col0, #T_3a730_row11_col1, #T_3a730_row11_col2, #T_3a730_row11_col3, #T_3a730_row11_col4, #T_3a730_row12_col0, #T_3a730_row12_col1, #T_3a730_row12_col2, #T_3a730_row12_col3, #T_3a730_row12_col4, #T_3a730_row13_col0, #T_3a730_row13_col1, #T_3a730_row13_col2, #T_3a730_row13_col3, #T_3a730_row13_col4, #T_3a730_row14_col0, #T_3a730_row14_col1, #T_3a730_row14_col2, #T_3a730_row14_col3, #T_3a730_row14_col4, #T_3a730_row15_col0, #T_3a730_row15_col1, #T_3a730_row15_col2, #T_3a730_row15_col3, #T_3a730_row15_col4, #T_3a730_row16_col0, #T_3a730_row16_col1, #T_3a730_row16_col2, #T_3a730_row16_col3, #T_3a730_row16_col4, #T_3a730_row17_col0, #T_3a730_row17_col1, #T_3a730_row17_col2, #T_3a730_row17_col3, #T_3a730_row17_col4, #T_3a730_row18_col0, #T_3a730_row18_col1, #T_3a730_row18_col2, #T_3a730_row18_col3, #T_3a730_row18_col4, #T_3a730_row19_col0, #T_3a730_row19_col1, #T_3a730_row19_col2, #T_3a730_row19_col3, #T_3a730_row19_col4, #T_3a730_row20_col0, #T_3a730_row20_col1, #T_3a730_row20_col2, #T_3a730_row20_col3, #T_3a730_row20_col4, #T_3a730_row21_col0, #T_3a730_row21_col1, #T_3a730_row21_col2, #T_3a730_row21_col3, #T_3a730_row21_col4, #T_3a730_row22_col0, #T_3a730_row22_col1, #T_3a730_row22_col2, #T_3a730_row22_col3, #T_3a730_row22_col4, #T_3a730_row23_col0, #T_3a730_row23_col1, #T_3a730_row23_col2, #T_3a730_row23_col3, #T_3a730_row23_col4, #T_3a730_row24_col0, #T_3a730_row24_col1, #T_3a730_row24_col2, #T_3a730_row24_col3, #T_3a730_row24_col4, #T_3a730_row25_col0, #T_3a730_row25_col1, #T_3a730_row25_col2, #T_3a730_row25_col3, #T_3a730_row25_col4, #T_3a730_row26_col0, #T_3a730_row26_col1, #T_3a730_row26_col2, #T_3a730_row26_col3, #T_3a730_row26_col4, #T_3a730_row27_col0, #T_3a730_row27_col1, #T_3a730_row27_col2, #T_3a730_row27_col3, #T_3a730_row27_col4 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3a730\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_3a730_level0_col0\" class=\"col_heading level0 col0\" >Test Suite ID</th>\n",
       "      <th id=\"T_3a730_level0_col1\" class=\"col_heading level0 col1\" >Test Suite Name</th>\n",
       "      <th id=\"T_3a730_level0_col2\" class=\"col_heading level0 col2\" >Test Suite Section</th>\n",
       "      <th id=\"T_3a730_level0_col3\" class=\"col_heading level0 col3\" >Test ID</th>\n",
       "      <th id=\"T_3a730_level0_col4\" class=\"col_heading level0 col4\" >Test Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row0_col0\" class=\"data row0 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row0_col1\" class=\"data row0 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row0_col2\" class=\"data row0 col2\" >tabular_dataset_description</td>\n",
       "      <td id=\"T_3a730_row0_col3\" class=\"data row0 col3\" >validmind.data_validation.DatasetDescription</td>\n",
       "      <td id=\"T_3a730_row0_col4\" class=\"data row0 col4\" >Dataset Description</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row1_col0\" class=\"data row1 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row1_col1\" class=\"data row1 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row1_col2\" class=\"data row1 col2\" >tabular_dataset_description</td>\n",
       "      <td id=\"T_3a730_row1_col3\" class=\"data row1 col3\" >validmind.data_validation.DescriptiveStatistics</td>\n",
       "      <td id=\"T_3a730_row1_col4\" class=\"data row1 col4\" >Descriptive Statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row2_col0\" class=\"data row2 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row2_col1\" class=\"data row2 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row2_col2\" class=\"data row2 col2\" >tabular_dataset_description</td>\n",
       "      <td id=\"T_3a730_row2_col3\" class=\"data row2 col3\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
       "      <td id=\"T_3a730_row2_col4\" class=\"data row2 col4\" >Pearson Correlation Matrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row3_col0\" class=\"data row3 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row3_col1\" class=\"data row3 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row3_col2\" class=\"data row3 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row3_col3\" class=\"data row3 col3\" >validmind.data_validation.ClassImbalance</td>\n",
       "      <td id=\"T_3a730_row3_col4\" class=\"data row3 col4\" >Class Imbalance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row4_col0\" class=\"data row4 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row4_col1\" class=\"data row4 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row4_col2\" class=\"data row4 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row4_col3\" class=\"data row4 col3\" >validmind.data_validation.Duplicates</td>\n",
       "      <td id=\"T_3a730_row4_col4\" class=\"data row4 col4\" >Duplicates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row5_col0\" class=\"data row5 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row5_col1\" class=\"data row5 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row5_col2\" class=\"data row5 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row5_col3\" class=\"data row5 col3\" >validmind.data_validation.HighCardinality</td>\n",
       "      <td id=\"T_3a730_row5_col4\" class=\"data row5 col4\" >High Cardinality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row6_col0\" class=\"data row6 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row6_col1\" class=\"data row6 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row6_col2\" class=\"data row6 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row6_col3\" class=\"data row6 col3\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
       "      <td id=\"T_3a730_row6_col4\" class=\"data row6 col4\" >High Pearson Correlation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row7_col0\" class=\"data row7 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row7_col1\" class=\"data row7 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row7_col2\" class=\"data row7 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row7_col3\" class=\"data row7 col3\" >validmind.data_validation.MissingValues</td>\n",
       "      <td id=\"T_3a730_row7_col4\" class=\"data row7 col4\" >Missing Values</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row8_col0\" class=\"data row8 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row8_col1\" class=\"data row8 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row8_col2\" class=\"data row8 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row8_col3\" class=\"data row8 col3\" >validmind.data_validation.Skewness</td>\n",
       "      <td id=\"T_3a730_row8_col4\" class=\"data row8 col4\" >Skewness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row9_col0\" class=\"data row9 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row9_col1\" class=\"data row9 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row9_col2\" class=\"data row9 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row9_col3\" class=\"data row9 col3\" >validmind.data_validation.UniqueRows</td>\n",
       "      <td id=\"T_3a730_row9_col4\" class=\"data row9 col4\" >Unique Rows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row10_col0\" class=\"data row10 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row10_col1\" class=\"data row10 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row10_col2\" class=\"data row10 col2\" >tabular_data_quality</td>\n",
       "      <td id=\"T_3a730_row10_col3\" class=\"data row10 col3\" >validmind.data_validation.TooManyZeroValues</td>\n",
       "      <td id=\"T_3a730_row10_col4\" class=\"data row10 col4\" >Too Many Zero Values</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row11_col0\" class=\"data row11 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row11_col1\" class=\"data row11 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row11_col2\" class=\"data row11 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row11_col3\" class=\"data row11 col3\" >validmind.model_validation.ModelMetadata</td>\n",
       "      <td id=\"T_3a730_row11_col4\" class=\"data row11 col4\" >Model Metadata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row12_col0\" class=\"data row12 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row12_col1\" class=\"data row12 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row12_col2\" class=\"data row12 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row12_col3\" class=\"data row12 col3\" >validmind.data_validation.DatasetSplit</td>\n",
       "      <td id=\"T_3a730_row12_col4\" class=\"data row12 col4\" >Dataset Split</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row13_col0\" class=\"data row13 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row13_col1\" class=\"data row13 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row13_col2\" class=\"data row13 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row13_col3\" class=\"data row13 col3\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
       "      <td id=\"T_3a730_row13_col4\" class=\"data row13 col4\" >Confusion Matrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row14_col0\" class=\"data row14 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row14_col1\" class=\"data row14 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row14_col2\" class=\"data row14 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row14_col3\" class=\"data row14 col3\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
       "      <td id=\"T_3a730_row14_col4\" class=\"data row14 col4\" >Classifier Performance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row15_col0\" class=\"data row15 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row15_col1\" class=\"data row15 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row15_col2\" class=\"data row15 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row15_col3\" class=\"data row15 col3\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
       "      <td id=\"T_3a730_row15_col4\" class=\"data row15 col4\" >Permutation Feature Importance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row16_col0\" class=\"data row16 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row16_col1\" class=\"data row16 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row16_col2\" class=\"data row16 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row16_col3\" class=\"data row16 col3\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
       "      <td id=\"T_3a730_row16_col4\" class=\"data row16 col4\" >Precision Recall Curve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row17_col0\" class=\"data row17 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row17_col1\" class=\"data row17 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row17_col2\" class=\"data row17 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row17_col3\" class=\"data row17 col3\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
       "      <td id=\"T_3a730_row17_col4\" class=\"data row17 col4\" >ROC Curve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row18_col0\" class=\"data row18 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row18_col1\" class=\"data row18 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row18_col2\" class=\"data row18 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row18_col3\" class=\"data row18 col3\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
       "      <td id=\"T_3a730_row18_col4\" class=\"data row18 col4\" >Population Stability Index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row19_col0\" class=\"data row19 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row19_col1\" class=\"data row19 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row19_col2\" class=\"data row19 col2\" >classifier_metrics</td>\n",
       "      <td id=\"T_3a730_row19_col3\" class=\"data row19 col3\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
       "      <td id=\"T_3a730_row19_col4\" class=\"data row19 col4\" >SHAP Global Importance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row20_col0\" class=\"data row20 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row20_col1\" class=\"data row20 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row20_col2\" class=\"data row20 col2\" >classifier_validation</td>\n",
       "      <td id=\"T_3a730_row20_col3\" class=\"data row20 col3\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
       "      <td id=\"T_3a730_row20_col4\" class=\"data row20 col4\" >Minimum Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row21_col0\" class=\"data row21 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row21_col1\" class=\"data row21 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row21_col2\" class=\"data row21 col2\" >classifier_validation</td>\n",
       "      <td id=\"T_3a730_row21_col3\" class=\"data row21 col3\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
       "      <td id=\"T_3a730_row21_col4\" class=\"data row21 col4\" >Minimum F1 Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row22_col0\" class=\"data row22 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row22_col1\" class=\"data row22 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row22_col2\" class=\"data row22 col2\" >classifier_validation</td>\n",
       "      <td id=\"T_3a730_row22_col3\" class=\"data row22 col3\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
       "      <td id=\"T_3a730_row22_col4\" class=\"data row22 col4\" >Minimum ROCAUC Score</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row23_col0\" class=\"data row23 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row23_col1\" class=\"data row23 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row23_col2\" class=\"data row23 col2\" >classifier_validation</td>\n",
       "      <td id=\"T_3a730_row23_col3\" class=\"data row23 col3\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
       "      <td id=\"T_3a730_row23_col4\" class=\"data row23 col4\" >Training Test Degradation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row24_col0\" class=\"data row24 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row24_col1\" class=\"data row24 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row24_col2\" class=\"data row24 col2\" >classifier_validation</td>\n",
       "      <td id=\"T_3a730_row24_col3\" class=\"data row24 col3\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
       "      <td id=\"T_3a730_row24_col4\" class=\"data row24 col4\" >Models Performance Comparison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row25_col0\" class=\"data row25 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row25_col1\" class=\"data row25 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row25_col2\" class=\"data row25 col2\" >classifier_model_diagnosis</td>\n",
       "      <td id=\"T_3a730_row25_col3\" class=\"data row25 col3\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
       "      <td id=\"T_3a730_row25_col4\" class=\"data row25 col4\" >Overfit Diagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row26_col0\" class=\"data row26 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row26_col1\" class=\"data row26 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row26_col2\" class=\"data row26 col2\" >classifier_model_diagnosis</td>\n",
       "      <td id=\"T_3a730_row26_col3\" class=\"data row26 col3\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
       "      <td id=\"T_3a730_row26_col4\" class=\"data row26 col4\" >Weakspots Diagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3a730_row27_col0\" class=\"data row27 col0\" >classifier_full_suite</td>\n",
       "      <td id=\"T_3a730_row27_col1\" class=\"data row27 col1\" >ClassifierFullSuite</td>\n",
       "      <td id=\"T_3a730_row27_col2\" class=\"data row27 col2\" >classifier_model_diagnosis</td>\n",
       "      <td id=\"T_3a730_row27_col3\" class=\"data row27 col3\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "      <td id=\"T_3a730_row27_col4\" class=\"data row27 col4\" >Robustness Diagnosis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x36b464c40>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vm.test_suites.describe_suite(\"classifier_full_suite\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc5_'></a>\n",
    "\n",
    "## List all tests\n",
    "\n",
    "To get the list of tests and their purpose:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3c6b1 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_3c6b1_row0_col0, #T_3c6b1_row0_col1, #T_3c6b1_row0_col2, #T_3c6b1_row0_col3, #T_3c6b1_row0_col4, #T_3c6b1_row1_col0, #T_3c6b1_row1_col1, #T_3c6b1_row1_col2, #T_3c6b1_row1_col3, #T_3c6b1_row1_col4, #T_3c6b1_row2_col0, #T_3c6b1_row2_col1, #T_3c6b1_row2_col2, #T_3c6b1_row2_col3, #T_3c6b1_row2_col4, #T_3c6b1_row3_col0, #T_3c6b1_row3_col1, #T_3c6b1_row3_col2, #T_3c6b1_row3_col3, #T_3c6b1_row3_col4, #T_3c6b1_row4_col0, #T_3c6b1_row4_col1, #T_3c6b1_row4_col2, #T_3c6b1_row4_col3, #T_3c6b1_row4_col4, #T_3c6b1_row5_col0, #T_3c6b1_row5_col1, #T_3c6b1_row5_col2, #T_3c6b1_row5_col3, #T_3c6b1_row5_col4, #T_3c6b1_row6_col0, #T_3c6b1_row6_col1, #T_3c6b1_row6_col2, #T_3c6b1_row6_col3, #T_3c6b1_row6_col4, #T_3c6b1_row7_col0, #T_3c6b1_row7_col1, #T_3c6b1_row7_col2, #T_3c6b1_row7_col3, #T_3c6b1_row7_col4, #T_3c6b1_row8_col0, #T_3c6b1_row8_col1, #T_3c6b1_row8_col2, #T_3c6b1_row8_col3, #T_3c6b1_row8_col4, #T_3c6b1_row9_col0, #T_3c6b1_row9_col1, #T_3c6b1_row9_col2, #T_3c6b1_row9_col3, #T_3c6b1_row9_col4, #T_3c6b1_row10_col0, #T_3c6b1_row10_col1, #T_3c6b1_row10_col2, #T_3c6b1_row10_col3, #T_3c6b1_row10_col4, #T_3c6b1_row11_col0, #T_3c6b1_row11_col1, #T_3c6b1_row11_col2, #T_3c6b1_row11_col3, #T_3c6b1_row11_col4, #T_3c6b1_row12_col0, #T_3c6b1_row12_col1, #T_3c6b1_row12_col2, #T_3c6b1_row12_col3, #T_3c6b1_row12_col4, #T_3c6b1_row13_col0, #T_3c6b1_row13_col1, #T_3c6b1_row13_col2, #T_3c6b1_row13_col3, #T_3c6b1_row13_col4, #T_3c6b1_row14_col0, #T_3c6b1_row14_col1, #T_3c6b1_row14_col2, #T_3c6b1_row14_col3, #T_3c6b1_row14_col4, #T_3c6b1_row15_col0, #T_3c6b1_row15_col1, #T_3c6b1_row15_col2, #T_3c6b1_row15_col3, #T_3c6b1_row15_col4, #T_3c6b1_row16_col0, #T_3c6b1_row16_col1, #T_3c6b1_row16_col2, #T_3c6b1_row16_col3, #T_3c6b1_row16_col4, #T_3c6b1_row17_col0, #T_3c6b1_row17_col1, #T_3c6b1_row17_col2, #T_3c6b1_row17_col3, #T_3c6b1_row17_col4, #T_3c6b1_row18_col0, #T_3c6b1_row18_col1, #T_3c6b1_row18_col2, #T_3c6b1_row18_col3, #T_3c6b1_row18_col4, #T_3c6b1_row19_col0, #T_3c6b1_row19_col1, #T_3c6b1_row19_col2, #T_3c6b1_row19_col3, #T_3c6b1_row19_col4, #T_3c6b1_row20_col0, #T_3c6b1_row20_col1, #T_3c6b1_row20_col2, #T_3c6b1_row20_col3, #T_3c6b1_row20_col4, #T_3c6b1_row21_col0, #T_3c6b1_row21_col1, #T_3c6b1_row21_col2, #T_3c6b1_row21_col3, #T_3c6b1_row21_col4, #T_3c6b1_row22_col0, #T_3c6b1_row22_col1, #T_3c6b1_row22_col2, #T_3c6b1_row22_col3, #T_3c6b1_row22_col4, #T_3c6b1_row23_col0, #T_3c6b1_row23_col1, #T_3c6b1_row23_col2, #T_3c6b1_row23_col3, #T_3c6b1_row23_col4, #T_3c6b1_row24_col0, #T_3c6b1_row24_col1, #T_3c6b1_row24_col2, #T_3c6b1_row24_col3, #T_3c6b1_row24_col4, #T_3c6b1_row25_col0, #T_3c6b1_row25_col1, #T_3c6b1_row25_col2, #T_3c6b1_row25_col3, #T_3c6b1_row25_col4, #T_3c6b1_row26_col0, #T_3c6b1_row26_col1, #T_3c6b1_row26_col2, #T_3c6b1_row26_col3, #T_3c6b1_row26_col4, #T_3c6b1_row27_col0, #T_3c6b1_row27_col1, #T_3c6b1_row27_col2, #T_3c6b1_row27_col3, #T_3c6b1_row27_col4, #T_3c6b1_row28_col0, #T_3c6b1_row28_col1, #T_3c6b1_row28_col2, #T_3c6b1_row28_col3, #T_3c6b1_row28_col4, #T_3c6b1_row29_col0, #T_3c6b1_row29_col1, #T_3c6b1_row29_col2, #T_3c6b1_row29_col3, #T_3c6b1_row29_col4, #T_3c6b1_row30_col0, #T_3c6b1_row30_col1, #T_3c6b1_row30_col2, #T_3c6b1_row30_col3, #T_3c6b1_row30_col4, #T_3c6b1_row31_col0, #T_3c6b1_row31_col1, #T_3c6b1_row31_col2, #T_3c6b1_row31_col3, #T_3c6b1_row31_col4, #T_3c6b1_row32_col0, #T_3c6b1_row32_col1, #T_3c6b1_row32_col2, #T_3c6b1_row32_col3, #T_3c6b1_row32_col4, #T_3c6b1_row33_col0, #T_3c6b1_row33_col1, #T_3c6b1_row33_col2, #T_3c6b1_row33_col3, #T_3c6b1_row33_col4, #T_3c6b1_row34_col0, #T_3c6b1_row34_col1, #T_3c6b1_row34_col2, #T_3c6b1_row34_col3, #T_3c6b1_row34_col4, #T_3c6b1_row35_col0, #T_3c6b1_row35_col1, #T_3c6b1_row35_col2, #T_3c6b1_row35_col3, #T_3c6b1_row35_col4, #T_3c6b1_row36_col0, #T_3c6b1_row36_col1, #T_3c6b1_row36_col2, #T_3c6b1_row36_col3, #T_3c6b1_row36_col4, #T_3c6b1_row37_col0, #T_3c6b1_row37_col1, #T_3c6b1_row37_col2, #T_3c6b1_row37_col3, #T_3c6b1_row37_col4, #T_3c6b1_row38_col0, #T_3c6b1_row38_col1, #T_3c6b1_row38_col2, #T_3c6b1_row38_col3, #T_3c6b1_row38_col4, #T_3c6b1_row39_col0, #T_3c6b1_row39_col1, #T_3c6b1_row39_col2, #T_3c6b1_row39_col3, #T_3c6b1_row39_col4, #T_3c6b1_row40_col0, #T_3c6b1_row40_col1, #T_3c6b1_row40_col2, #T_3c6b1_row40_col3, #T_3c6b1_row40_col4, #T_3c6b1_row41_col0, #T_3c6b1_row41_col1, #T_3c6b1_row41_col2, #T_3c6b1_row41_col3, #T_3c6b1_row41_col4, #T_3c6b1_row42_col0, #T_3c6b1_row42_col1, #T_3c6b1_row42_col2, #T_3c6b1_row42_col3, #T_3c6b1_row42_col4, #T_3c6b1_row43_col0, #T_3c6b1_row43_col1, #T_3c6b1_row43_col2, #T_3c6b1_row43_col3, #T_3c6b1_row43_col4, #T_3c6b1_row44_col0, #T_3c6b1_row44_col1, #T_3c6b1_row44_col2, #T_3c6b1_row44_col3, #T_3c6b1_row44_col4, #T_3c6b1_row45_col0, #T_3c6b1_row45_col1, #T_3c6b1_row45_col2, #T_3c6b1_row45_col3, #T_3c6b1_row45_col4, #T_3c6b1_row46_col0, #T_3c6b1_row46_col1, #T_3c6b1_row46_col2, #T_3c6b1_row46_col3, #T_3c6b1_row46_col4, #T_3c6b1_row47_col0, #T_3c6b1_row47_col1, #T_3c6b1_row47_col2, #T_3c6b1_row47_col3, #T_3c6b1_row47_col4, #T_3c6b1_row48_col0, #T_3c6b1_row48_col1, #T_3c6b1_row48_col2, #T_3c6b1_row48_col3, #T_3c6b1_row48_col4, #T_3c6b1_row49_col0, #T_3c6b1_row49_col1, #T_3c6b1_row49_col2, #T_3c6b1_row49_col3, #T_3c6b1_row49_col4, #T_3c6b1_row50_col0, #T_3c6b1_row50_col1, #T_3c6b1_row50_col2, #T_3c6b1_row50_col3, #T_3c6b1_row50_col4, #T_3c6b1_row51_col0, #T_3c6b1_row51_col1, #T_3c6b1_row51_col2, #T_3c6b1_row51_col3, #T_3c6b1_row51_col4, #T_3c6b1_row52_col0, #T_3c6b1_row52_col1, #T_3c6b1_row52_col2, #T_3c6b1_row52_col3, #T_3c6b1_row52_col4, #T_3c6b1_row53_col0, #T_3c6b1_row53_col1, #T_3c6b1_row53_col2, #T_3c6b1_row53_col3, #T_3c6b1_row53_col4, #T_3c6b1_row54_col0, #T_3c6b1_row54_col1, #T_3c6b1_row54_col2, #T_3c6b1_row54_col3, #T_3c6b1_row54_col4, #T_3c6b1_row55_col0, #T_3c6b1_row55_col1, #T_3c6b1_row55_col2, #T_3c6b1_row55_col3, #T_3c6b1_row55_col4, #T_3c6b1_row56_col0, #T_3c6b1_row56_col1, #T_3c6b1_row56_col2, #T_3c6b1_row56_col3, #T_3c6b1_row56_col4, #T_3c6b1_row57_col0, #T_3c6b1_row57_col1, #T_3c6b1_row57_col2, #T_3c6b1_row57_col3, #T_3c6b1_row57_col4, #T_3c6b1_row58_col0, #T_3c6b1_row58_col1, #T_3c6b1_row58_col2, #T_3c6b1_row58_col3, #T_3c6b1_row58_col4, #T_3c6b1_row59_col0, #T_3c6b1_row59_col1, #T_3c6b1_row59_col2, #T_3c6b1_row59_col3, #T_3c6b1_row59_col4, #T_3c6b1_row60_col0, #T_3c6b1_row60_col1, #T_3c6b1_row60_col2, #T_3c6b1_row60_col3, #T_3c6b1_row60_col4, #T_3c6b1_row61_col0, #T_3c6b1_row61_col1, #T_3c6b1_row61_col2, #T_3c6b1_row61_col3, #T_3c6b1_row61_col4, #T_3c6b1_row62_col0, #T_3c6b1_row62_col1, #T_3c6b1_row62_col2, #T_3c6b1_row62_col3, #T_3c6b1_row62_col4, #T_3c6b1_row63_col0, #T_3c6b1_row63_col1, #T_3c6b1_row63_col2, #T_3c6b1_row63_col3, #T_3c6b1_row63_col4, #T_3c6b1_row64_col0, #T_3c6b1_row64_col1, #T_3c6b1_row64_col2, #T_3c6b1_row64_col3, #T_3c6b1_row64_col4, #T_3c6b1_row65_col0, #T_3c6b1_row65_col1, #T_3c6b1_row65_col2, #T_3c6b1_row65_col3, #T_3c6b1_row65_col4, #T_3c6b1_row66_col0, #T_3c6b1_row66_col1, #T_3c6b1_row66_col2, #T_3c6b1_row66_col3, #T_3c6b1_row66_col4, #T_3c6b1_row67_col0, #T_3c6b1_row67_col1, #T_3c6b1_row67_col2, #T_3c6b1_row67_col3, #T_3c6b1_row67_col4, #T_3c6b1_row68_col0, #T_3c6b1_row68_col1, #T_3c6b1_row68_col2, #T_3c6b1_row68_col3, #T_3c6b1_row68_col4, #T_3c6b1_row69_col0, #T_3c6b1_row69_col1, #T_3c6b1_row69_col2, #T_3c6b1_row69_col3, #T_3c6b1_row69_col4, #T_3c6b1_row70_col0, #T_3c6b1_row70_col1, #T_3c6b1_row70_col2, #T_3c6b1_row70_col3, #T_3c6b1_row70_col4, #T_3c6b1_row71_col0, #T_3c6b1_row71_col1, #T_3c6b1_row71_col2, #T_3c6b1_row71_col3, #T_3c6b1_row71_col4, #T_3c6b1_row72_col0, #T_3c6b1_row72_col1, #T_3c6b1_row72_col2, #T_3c6b1_row72_col3, #T_3c6b1_row72_col4, #T_3c6b1_row73_col0, #T_3c6b1_row73_col1, #T_3c6b1_row73_col2, #T_3c6b1_row73_col3, #T_3c6b1_row73_col4, #T_3c6b1_row74_col0, #T_3c6b1_row74_col1, #T_3c6b1_row74_col2, #T_3c6b1_row74_col3, #T_3c6b1_row74_col4, #T_3c6b1_row75_col0, #T_3c6b1_row75_col1, #T_3c6b1_row75_col2, #T_3c6b1_row75_col3, #T_3c6b1_row75_col4, #T_3c6b1_row76_col0, #T_3c6b1_row76_col1, #T_3c6b1_row76_col2, #T_3c6b1_row76_col3, #T_3c6b1_row76_col4, #T_3c6b1_row77_col0, #T_3c6b1_row77_col1, #T_3c6b1_row77_col2, #T_3c6b1_row77_col3, #T_3c6b1_row77_col4, #T_3c6b1_row78_col0, #T_3c6b1_row78_col1, #T_3c6b1_row78_col2, #T_3c6b1_row78_col3, #T_3c6b1_row78_col4, #T_3c6b1_row79_col0, #T_3c6b1_row79_col1, #T_3c6b1_row79_col2, #T_3c6b1_row79_col3, #T_3c6b1_row79_col4, #T_3c6b1_row80_col0, #T_3c6b1_row80_col1, #T_3c6b1_row80_col2, #T_3c6b1_row80_col3, #T_3c6b1_row80_col4, #T_3c6b1_row81_col0, #T_3c6b1_row81_col1, #T_3c6b1_row81_col2, #T_3c6b1_row81_col3, #T_3c6b1_row81_col4, #T_3c6b1_row82_col0, #T_3c6b1_row82_col1, #T_3c6b1_row82_col2, #T_3c6b1_row82_col3, #T_3c6b1_row82_col4, #T_3c6b1_row83_col0, #T_3c6b1_row83_col1, #T_3c6b1_row83_col2, #T_3c6b1_row83_col3, #T_3c6b1_row83_col4, #T_3c6b1_row84_col0, #T_3c6b1_row84_col1, #T_3c6b1_row84_col2, #T_3c6b1_row84_col3, #T_3c6b1_row84_col4, #T_3c6b1_row85_col0, #T_3c6b1_row85_col1, #T_3c6b1_row85_col2, #T_3c6b1_row85_col3, #T_3c6b1_row85_col4, #T_3c6b1_row86_col0, #T_3c6b1_row86_col1, #T_3c6b1_row86_col2, #T_3c6b1_row86_col3, #T_3c6b1_row86_col4, #T_3c6b1_row87_col0, #T_3c6b1_row87_col1, #T_3c6b1_row87_col2, #T_3c6b1_row87_col3, #T_3c6b1_row87_col4, #T_3c6b1_row88_col0, #T_3c6b1_row88_col1, #T_3c6b1_row88_col2, #T_3c6b1_row88_col3, #T_3c6b1_row88_col4, #T_3c6b1_row89_col0, #T_3c6b1_row89_col1, #T_3c6b1_row89_col2, #T_3c6b1_row89_col3, #T_3c6b1_row89_col4, #T_3c6b1_row90_col0, #T_3c6b1_row90_col1, #T_3c6b1_row90_col2, #T_3c6b1_row90_col3, #T_3c6b1_row90_col4, #T_3c6b1_row91_col0, #T_3c6b1_row91_col1, #T_3c6b1_row91_col2, #T_3c6b1_row91_col3, #T_3c6b1_row91_col4, #T_3c6b1_row92_col0, #T_3c6b1_row92_col1, #T_3c6b1_row92_col2, #T_3c6b1_row92_col3, #T_3c6b1_row92_col4, #T_3c6b1_row93_col0, #T_3c6b1_row93_col1, #T_3c6b1_row93_col2, #T_3c6b1_row93_col3, #T_3c6b1_row93_col4, #T_3c6b1_row94_col0, #T_3c6b1_row94_col1, #T_3c6b1_row94_col2, #T_3c6b1_row94_col3, #T_3c6b1_row94_col4, #T_3c6b1_row95_col0, #T_3c6b1_row95_col1, #T_3c6b1_row95_col2, #T_3c6b1_row95_col3, #T_3c6b1_row95_col4, #T_3c6b1_row96_col0, #T_3c6b1_row96_col1, #T_3c6b1_row96_col2, #T_3c6b1_row96_col3, #T_3c6b1_row96_col4, #T_3c6b1_row97_col0, #T_3c6b1_row97_col1, #T_3c6b1_row97_col2, #T_3c6b1_row97_col3, #T_3c6b1_row97_col4, #T_3c6b1_row98_col0, #T_3c6b1_row98_col1, #T_3c6b1_row98_col2, #T_3c6b1_row98_col3, #T_3c6b1_row98_col4, #T_3c6b1_row99_col0, #T_3c6b1_row99_col1, #T_3c6b1_row99_col2, #T_3c6b1_row99_col3, #T_3c6b1_row99_col4, #T_3c6b1_row100_col0, #T_3c6b1_row100_col1, #T_3c6b1_row100_col2, #T_3c6b1_row100_col3, #T_3c6b1_row100_col4, #T_3c6b1_row101_col0, #T_3c6b1_row101_col1, #T_3c6b1_row101_col2, #T_3c6b1_row101_col3, #T_3c6b1_row101_col4, #T_3c6b1_row102_col0, #T_3c6b1_row102_col1, #T_3c6b1_row102_col2, #T_3c6b1_row102_col3, #T_3c6b1_row102_col4, #T_3c6b1_row103_col0, #T_3c6b1_row103_col1, #T_3c6b1_row103_col2, #T_3c6b1_row103_col3, #T_3c6b1_row103_col4, #T_3c6b1_row104_col0, #T_3c6b1_row104_col1, #T_3c6b1_row104_col2, #T_3c6b1_row104_col3, #T_3c6b1_row104_col4, #T_3c6b1_row105_col0, #T_3c6b1_row105_col1, #T_3c6b1_row105_col2, #T_3c6b1_row105_col3, #T_3c6b1_row105_col4, #T_3c6b1_row106_col0, #T_3c6b1_row106_col1, #T_3c6b1_row106_col2, #T_3c6b1_row106_col3, #T_3c6b1_row106_col4, #T_3c6b1_row107_col0, #T_3c6b1_row107_col1, #T_3c6b1_row107_col2, #T_3c6b1_row107_col3, #T_3c6b1_row107_col4, #T_3c6b1_row108_col0, #T_3c6b1_row108_col1, #T_3c6b1_row108_col2, #T_3c6b1_row108_col3, #T_3c6b1_row108_col4, #T_3c6b1_row109_col0, #T_3c6b1_row109_col1, #T_3c6b1_row109_col2, #T_3c6b1_row109_col3, #T_3c6b1_row109_col4, #T_3c6b1_row110_col0, #T_3c6b1_row110_col1, #T_3c6b1_row110_col2, #T_3c6b1_row110_col3, #T_3c6b1_row110_col4, #T_3c6b1_row111_col0, #T_3c6b1_row111_col1, #T_3c6b1_row111_col2, #T_3c6b1_row111_col3, #T_3c6b1_row111_col4, #T_3c6b1_row112_col0, #T_3c6b1_row112_col1, #T_3c6b1_row112_col2, #T_3c6b1_row112_col3, #T_3c6b1_row112_col4, #T_3c6b1_row113_col0, #T_3c6b1_row113_col1, #T_3c6b1_row113_col2, #T_3c6b1_row113_col3, #T_3c6b1_row113_col4, #T_3c6b1_row114_col0, #T_3c6b1_row114_col1, #T_3c6b1_row114_col2, #T_3c6b1_row114_col3, #T_3c6b1_row114_col4, #T_3c6b1_row115_col0, #T_3c6b1_row115_col1, #T_3c6b1_row115_col2, #T_3c6b1_row115_col3, #T_3c6b1_row115_col4, #T_3c6b1_row116_col0, #T_3c6b1_row116_col1, #T_3c6b1_row116_col2, #T_3c6b1_row116_col3, #T_3c6b1_row116_col4, #T_3c6b1_row117_col0, #T_3c6b1_row117_col1, #T_3c6b1_row117_col2, #T_3c6b1_row117_col3, #T_3c6b1_row117_col4, #T_3c6b1_row118_col0, #T_3c6b1_row118_col1, #T_3c6b1_row118_col2, #T_3c6b1_row118_col3, #T_3c6b1_row118_col4, #T_3c6b1_row119_col0, #T_3c6b1_row119_col1, #T_3c6b1_row119_col2, #T_3c6b1_row119_col3, #T_3c6b1_row119_col4, #T_3c6b1_row120_col0, #T_3c6b1_row120_col1, #T_3c6b1_row120_col2, #T_3c6b1_row120_col3, #T_3c6b1_row120_col4, #T_3c6b1_row121_col0, #T_3c6b1_row121_col1, #T_3c6b1_row121_col2, #T_3c6b1_row121_col3, #T_3c6b1_row121_col4, #T_3c6b1_row122_col0, #T_3c6b1_row122_col1, #T_3c6b1_row122_col2, #T_3c6b1_row122_col3, #T_3c6b1_row122_col4, #T_3c6b1_row123_col0, #T_3c6b1_row123_col1, #T_3c6b1_row123_col2, #T_3c6b1_row123_col3, #T_3c6b1_row123_col4, #T_3c6b1_row124_col0, #T_3c6b1_row124_col1, #T_3c6b1_row124_col2, #T_3c6b1_row124_col3, #T_3c6b1_row124_col4, #T_3c6b1_row125_col0, #T_3c6b1_row125_col1, #T_3c6b1_row125_col2, #T_3c6b1_row125_col3, #T_3c6b1_row125_col4, #T_3c6b1_row126_col0, #T_3c6b1_row126_col1, #T_3c6b1_row126_col2, #T_3c6b1_row126_col3, #T_3c6b1_row126_col4, #T_3c6b1_row127_col0, #T_3c6b1_row127_col1, #T_3c6b1_row127_col2, #T_3c6b1_row127_col3, #T_3c6b1_row127_col4, #T_3c6b1_row128_col0, #T_3c6b1_row128_col1, #T_3c6b1_row128_col2, #T_3c6b1_row128_col3, #T_3c6b1_row128_col4, #T_3c6b1_row129_col0, #T_3c6b1_row129_col1, #T_3c6b1_row129_col2, #T_3c6b1_row129_col3, #T_3c6b1_row129_col4, #T_3c6b1_row130_col0, #T_3c6b1_row130_col1, #T_3c6b1_row130_col2, #T_3c6b1_row130_col3, #T_3c6b1_row130_col4, #T_3c6b1_row131_col0, #T_3c6b1_row131_col1, #T_3c6b1_row131_col2, #T_3c6b1_row131_col3, #T_3c6b1_row131_col4, #T_3c6b1_row132_col0, #T_3c6b1_row132_col1, #T_3c6b1_row132_col2, #T_3c6b1_row132_col3, #T_3c6b1_row132_col4, #T_3c6b1_row133_col0, #T_3c6b1_row133_col1, #T_3c6b1_row133_col2, #T_3c6b1_row133_col3, #T_3c6b1_row133_col4, #T_3c6b1_row134_col0, #T_3c6b1_row134_col1, #T_3c6b1_row134_col2, #T_3c6b1_row134_col3, #T_3c6b1_row134_col4, #T_3c6b1_row135_col0, #T_3c6b1_row135_col1, #T_3c6b1_row135_col2, #T_3c6b1_row135_col3, #T_3c6b1_row135_col4, #T_3c6b1_row136_col0, #T_3c6b1_row136_col1, #T_3c6b1_row136_col2, #T_3c6b1_row136_col3, #T_3c6b1_row136_col4, #T_3c6b1_row137_col0, #T_3c6b1_row137_col1, #T_3c6b1_row137_col2, #T_3c6b1_row137_col3, #T_3c6b1_row137_col4, #T_3c6b1_row138_col0, #T_3c6b1_row138_col1, #T_3c6b1_row138_col2, #T_3c6b1_row138_col3, #T_3c6b1_row138_col4, #T_3c6b1_row139_col0, #T_3c6b1_row139_col1, #T_3c6b1_row139_col2, #T_3c6b1_row139_col3, #T_3c6b1_row139_col4, #T_3c6b1_row140_col0, #T_3c6b1_row140_col1, #T_3c6b1_row140_col2, #T_3c6b1_row140_col3, #T_3c6b1_row140_col4, #T_3c6b1_row141_col0, #T_3c6b1_row141_col1, #T_3c6b1_row141_col2, #T_3c6b1_row141_col3, #T_3c6b1_row141_col4, #T_3c6b1_row142_col0, #T_3c6b1_row142_col1, #T_3c6b1_row142_col2, #T_3c6b1_row142_col3, #T_3c6b1_row142_col4, #T_3c6b1_row143_col0, #T_3c6b1_row143_col1, #T_3c6b1_row143_col2, #T_3c6b1_row143_col3, #T_3c6b1_row143_col4, #T_3c6b1_row144_col0, #T_3c6b1_row144_col1, #T_3c6b1_row144_col2, #T_3c6b1_row144_col3, #T_3c6b1_row144_col4, #T_3c6b1_row145_col0, #T_3c6b1_row145_col1, #T_3c6b1_row145_col2, #T_3c6b1_row145_col3, #T_3c6b1_row145_col4, #T_3c6b1_row146_col0, #T_3c6b1_row146_col1, #T_3c6b1_row146_col2, #T_3c6b1_row146_col3, #T_3c6b1_row146_col4, #T_3c6b1_row147_col0, #T_3c6b1_row147_col1, #T_3c6b1_row147_col2, #T_3c6b1_row147_col3, #T_3c6b1_row147_col4, #T_3c6b1_row148_col0, #T_3c6b1_row148_col1, #T_3c6b1_row148_col2, #T_3c6b1_row148_col3, #T_3c6b1_row148_col4, #T_3c6b1_row149_col0, #T_3c6b1_row149_col1, #T_3c6b1_row149_col2, #T_3c6b1_row149_col3, #T_3c6b1_row149_col4, #T_3c6b1_row150_col0, #T_3c6b1_row150_col1, #T_3c6b1_row150_col2, #T_3c6b1_row150_col3, #T_3c6b1_row150_col4, #T_3c6b1_row151_col0, #T_3c6b1_row151_col1, #T_3c6b1_row151_col2, #T_3c6b1_row151_col3, #T_3c6b1_row151_col4, #T_3c6b1_row152_col0, #T_3c6b1_row152_col1, #T_3c6b1_row152_col2, #T_3c6b1_row152_col3, #T_3c6b1_row152_col4, #T_3c6b1_row153_col0, #T_3c6b1_row153_col1, #T_3c6b1_row153_col2, #T_3c6b1_row153_col3, #T_3c6b1_row153_col4, #T_3c6b1_row154_col0, #T_3c6b1_row154_col1, #T_3c6b1_row154_col2, #T_3c6b1_row154_col3, #T_3c6b1_row154_col4, #T_3c6b1_row155_col0, #T_3c6b1_row155_col1, #T_3c6b1_row155_col2, #T_3c6b1_row155_col3, #T_3c6b1_row155_col4 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3c6b1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_3c6b1_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
       "      <th id=\"T_3c6b1_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_3c6b1_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_3c6b1_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
       "      <th id=\"T_3c6b1_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row0_col0\" class=\"data row0 col0\" >validmind.prompt_validation.Bias</td>\n",
       "      <td id=\"T_3c6b1_row0_col1\" class=\"data row0 col1\" >Bias</td>\n",
       "      <td id=\"T_3c6b1_row0_col2\" class=\"data row0 col2\" >Evaluates bias in a Large Language Model based on the order and distribution of exemplars in a prompt....</td>\n",
       "      <td id=\"T_3c6b1_row0_col3\" class=\"data row0 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row0_col4\" class=\"data row0 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row1_col0\" class=\"data row1 col0\" >validmind.prompt_validation.Clarity</td>\n",
       "      <td id=\"T_3c6b1_row1_col1\" class=\"data row1 col1\" >Clarity</td>\n",
       "      <td id=\"T_3c6b1_row1_col2\" class=\"data row1 col2\" >Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....</td>\n",
       "      <td id=\"T_3c6b1_row1_col3\" class=\"data row1 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row1_col4\" class=\"data row1 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row2_col0\" class=\"data row2 col0\" >validmind.prompt_validation.Specificity</td>\n",
       "      <td id=\"T_3c6b1_row2_col1\" class=\"data row2 col1\" >Specificity</td>\n",
       "      <td id=\"T_3c6b1_row2_col2\" class=\"data row2 col2\" >Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity,...</td>\n",
       "      <td id=\"T_3c6b1_row2_col3\" class=\"data row2 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row2_col4\" class=\"data row2 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row3_col0\" class=\"data row3 col0\" >validmind.prompt_validation.Robustness</td>\n",
       "      <td id=\"T_3c6b1_row3_col1\" class=\"data row3 col1\" >Robustness</td>\n",
       "      <td id=\"T_3c6b1_row3_col2\" class=\"data row3 col2\" >Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts....</td>\n",
       "      <td id=\"T_3c6b1_row3_col3\" class=\"data row3 col3\" >['model']</td>\n",
       "      <td id=\"T_3c6b1_row3_col4\" class=\"data row3 col4\" >{'num_tests': 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row4_col0\" class=\"data row4 col0\" >validmind.prompt_validation.NegativeInstruction</td>\n",
       "      <td id=\"T_3c6b1_row4_col1\" class=\"data row4 col1\" >Negative Instruction</td>\n",
       "      <td id=\"T_3c6b1_row4_col2\" class=\"data row4 col2\" >Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....</td>\n",
       "      <td id=\"T_3c6b1_row4_col3\" class=\"data row4 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row4_col4\" class=\"data row4 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row5_col0\" class=\"data row5 col0\" >validmind.prompt_validation.Conciseness</td>\n",
       "      <td id=\"T_3c6b1_row5_col1\" class=\"data row5 col1\" >Conciseness</td>\n",
       "      <td id=\"T_3c6b1_row5_col2\" class=\"data row5 col2\" >Analyzes and grades the conciseness of prompts provided to a Large Language Model....</td>\n",
       "      <td id=\"T_3c6b1_row5_col3\" class=\"data row5 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row5_col4\" class=\"data row5 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row6_col0\" class=\"data row6 col0\" >validmind.prompt_validation.Delimitation</td>\n",
       "      <td id=\"T_3c6b1_row6_col1\" class=\"data row6 col1\" >Delimitation</td>\n",
       "      <td id=\"T_3c6b1_row6_col2\" class=\"data row6 col2\" >Evaluates the proper use of delimiters in prompts provided to Large Language Models....</td>\n",
       "      <td id=\"T_3c6b1_row6_col3\" class=\"data row6 col3\" >['model.prompt']</td>\n",
       "      <td id=\"T_3c6b1_row6_col4\" class=\"data row6 col4\" >{'min_threshold': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.BertScore</td>\n",
       "      <td id=\"T_3c6b1_row7_col1\" class=\"data row7 col1\" >Bert Score</td>\n",
       "      <td id=\"T_3c6b1_row7_col2\" class=\"data row7 col2\" >Evaluates the quality of machine-generated text using BERTScore metrics and visualizes the results through histograms...</td>\n",
       "      <td id=\"T_3c6b1_row7_col3\" class=\"data row7 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row7_col4\" class=\"data row7 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.RegardScore</td>\n",
       "      <td id=\"T_3c6b1_row8_col1\" class=\"data row8 col1\" >Regard Score</td>\n",
       "      <td id=\"T_3c6b1_row8_col2\" class=\"data row8 col2\" >Computes and visualizes the regard score for each text instance, assessing sentiment and potential biases....</td>\n",
       "      <td id=\"T_3c6b1_row8_col3\" class=\"data row8 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row8_col4\" class=\"data row8 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.BleuScore</td>\n",
       "      <td id=\"T_3c6b1_row9_col1\" class=\"data row9 col1\" >Bleu Score</td>\n",
       "      <td id=\"T_3c6b1_row9_col2\" class=\"data row9 col2\" >Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...</td>\n",
       "      <td id=\"T_3c6b1_row9_col3\" class=\"data row9 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
       "      <td id=\"T_3c6b1_row10_col1\" class=\"data row10 col1\" >Regression Residuals Plot</td>\n",
       "      <td id=\"T_3c6b1_row10_col2\" class=\"data row10 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
       "      <td id=\"T_3c6b1_row10_col3\" class=\"data row10 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row10_col4\" class=\"data row10 col4\" >{'bin_size': 0.1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
       "      <td id=\"T_3c6b1_row11_col1\" class=\"data row11 col1\" >Features AUC</td>\n",
       "      <td id=\"T_3c6b1_row11_col2\" class=\"data row11 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating the Area Under the Curve (AUC) for each feature separately....</td>\n",
       "      <td id=\"T_3c6b1_row11_col3\" class=\"data row11 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row11_col4\" class=\"data row11 col4\" >{'fontsize': 12, 'figure_height': 500}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.ContextualRecall</td>\n",
       "      <td id=\"T_3c6b1_row12_col1\" class=\"data row12 col1\" >Contextual Recall</td>\n",
       "      <td id=\"T_3c6b1_row12_col2\" class=\"data row12 col2\" >Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct text, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of descriptive statistics for contextual recall scores....</td>\n",
       "      <td id=\"T_3c6b1_row12_col3\" class=\"data row12 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row12_col4\" class=\"data row12 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.MeteorScore</td>\n",
       "      <td id=\"T_3c6b1_row13_col1\" class=\"data row13 col1\" >Meteor Score</td>\n",
       "      <td id=\"T_3c6b1_row13_col2\" class=\"data row13 col2\" >Computes and visualizes the METEOR score for each text generation instance, assessing translation quality....</td>\n",
       "      <td id=\"T_3c6b1_row13_col3\" class=\"data row13 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row13_col4\" class=\"data row13 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.RougeScore</td>\n",
       "      <td id=\"T_3c6b1_row14_col1\" class=\"data row14 col1\" >Rouge Score</td>\n",
       "      <td id=\"T_3c6b1_row14_col2\" class=\"data row14 col2\" >Evaluates the quality of machine-generated text using ROUGE metrics and visualizes the results through histograms...</td>\n",
       "      <td id=\"T_3c6b1_row14_col3\" class=\"data row14 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row14_col4\" class=\"data row14 col4\" >{'metric': 'rouge-1'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.ModelMetadata</td>\n",
       "      <td id=\"T_3c6b1_row15_col1\" class=\"data row15 col1\" >Model Metadata</td>\n",
       "      <td id=\"T_3c6b1_row15_col2\" class=\"data row15 col2\" >Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis....</td>\n",
       "      <td id=\"T_3c6b1_row15_col3\" class=\"data row15 col3\" >['model']</td>\n",
       "      <td id=\"T_3c6b1_row15_col4\" class=\"data row15 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
       "      <td id=\"T_3c6b1_row16_col1\" class=\"data row16 col1\" >Cluster Size Distribution</td>\n",
       "      <td id=\"T_3c6b1_row16_col2\" class=\"data row16 col2\" >Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing...</td>\n",
       "      <td id=\"T_3c6b1_row16_col3\" class=\"data row16 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row16_col4\" class=\"data row16 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.TokenDisparity</td>\n",
       "      <td id=\"T_3c6b1_row17_col1\" class=\"data row17 col1\" >Token Disparity</td>\n",
       "      <td id=\"T_3c6b1_row17_col2\" class=\"data row17 col2\" >Evaluates the token disparity between reference and generated texts, visualizing the results through histograms...</td>\n",
       "      <td id=\"T_3c6b1_row17_col3\" class=\"data row17 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row17_col4\" class=\"data row17 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.ToxicityScore</td>\n",
       "      <td id=\"T_3c6b1_row18_col1\" class=\"data row18 col1\" >Toxicity Score</td>\n",
       "      <td id=\"T_3c6b1_row18_col2\" class=\"data row18 col2\" >Computes and visualizes the toxicity score for input text, true text, and predicted text, assessing content quality and potential risk....</td>\n",
       "      <td id=\"T_3c6b1_row18_col3\" class=\"data row18 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row18_col4\" class=\"data row18 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.embeddings.CosineSimilarityComparison</td>\n",
       "      <td id=\"T_3c6b1_row19_col1\" class=\"data row19 col1\" >Cosine Similarity Comparison</td>\n",
       "      <td id=\"T_3c6b1_row19_col2\" class=\"data row19 col2\" >Computes pairwise cosine similarities between model embeddings and visualizes the results through bar charts,...</td>\n",
       "      <td id=\"T_3c6b1_row19_col3\" class=\"data row19 col3\" >['dataset', 'models']</td>\n",
       "      <td id=\"T_3c6b1_row19_col4\" class=\"data row19 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
       "      <td id=\"T_3c6b1_row20_col1\" class=\"data row20 col1\" >Embeddings Visualization2 D</td>\n",
       "      <td id=\"T_3c6b1_row20_col2\" class=\"data row20 col2\" >Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....</td>\n",
       "      <td id=\"T_3c6b1_row20_col3\" class=\"data row20 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row20_col4\" class=\"data row20 col4\" >{'cluster_column': None, 'perplexity': 30}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise</td>\n",
       "      <td id=\"T_3c6b1_row21_col1\" class=\"data row21 col1\" >Stability Analysis Random Noise</td>\n",
       "      <td id=\"T_3c6b1_row21_col2\" class=\"data row21 col2\" >Evaluate robustness of embeddings models to random noise introduced by using...</td>\n",
       "      <td id=\"T_3c6b1_row21_col3\" class=\"data row21 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row21_col4\" class=\"data row21 col4\" >{'mean_similarity_threshold': 0.7, 'probability': 0.02}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.embeddings.TSNEComponentsPairwisePlots</td>\n",
       "      <td id=\"T_3c6b1_row22_col1\" class=\"data row22 col1\" >TSNE Components Pairwise Plots</td>\n",
       "      <td id=\"T_3c6b1_row22_col2\" class=\"data row22 col2\" >Plots individual scatter plots for pairwise combinations of t-SNE components of embeddings....</td>\n",
       "      <td id=\"T_3c6b1_row22_col3\" class=\"data row22 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row22_col4\" class=\"data row22 col4\" >{'n_components': 2, 'perplexity': 30, 'title': 't-SNE'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.embeddings.CosineSimilarityDistribution</td>\n",
       "      <td id=\"T_3c6b1_row23_col1\" class=\"data row23 col1\" >Cosine Similarity Distribution</td>\n",
       "      <td id=\"T_3c6b1_row23_col2\" class=\"data row23 col2\" >Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...</td>\n",
       "      <td id=\"T_3c6b1_row23_col3\" class=\"data row23 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row23_col4\" class=\"data row23 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.embeddings.PCAComponentsPairwisePlots</td>\n",
       "      <td id=\"T_3c6b1_row24_col1\" class=\"data row24 col1\" >PCA Components Pairwise Plots</td>\n",
       "      <td id=\"T_3c6b1_row24_col2\" class=\"data row24 col2\" >Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model embeddings....</td>\n",
       "      <td id=\"T_3c6b1_row24_col3\" class=\"data row24 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row24_col4\" class=\"data row24 col4\" >{'n_components': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.embeddings.CosineSimilarityHeatmap</td>\n",
       "      <td id=\"T_3c6b1_row25_col1\" class=\"data row25 col1\" >Cosine Similarity Heatmap</td>\n",
       "      <td id=\"T_3c6b1_row25_col2\" class=\"data row25 col2\" >Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....</td>\n",
       "      <td id=\"T_3c6b1_row25_col3\" class=\"data row25 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row25_col4\" class=\"data row25 col4\" >{'title': 'Cosine Similarity Matrix', 'color': 'Cosine Similarity', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
       "      <td id=\"T_3c6b1_row26_col1\" class=\"data row26 col1\" >Stability Analysis Translation</td>\n",
       "      <td id=\"T_3c6b1_row26_col2\" class=\"data row26 col2\" >Evaluate robustness of embeddings models to noise introduced by translating...</td>\n",
       "      <td id=\"T_3c6b1_row26_col3\" class=\"data row26 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row26_col4\" class=\"data row26 col4\" >{'source_lang': 'en', 'target_lang': 'fr', 'mean_similarity_threshold': 0.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.embeddings.EuclideanDistanceComparison</td>\n",
       "      <td id=\"T_3c6b1_row27_col1\" class=\"data row27 col1\" >Euclidean Distance Comparison</td>\n",
       "      <td id=\"T_3c6b1_row27_col2\" class=\"data row27 col2\" >Computes pairwise Euclidean distances between model embeddings and visualizes the results through bar charts,...</td>\n",
       "      <td id=\"T_3c6b1_row27_col3\" class=\"data row27 col3\" >['dataset', 'models']</td>\n",
       "      <td id=\"T_3c6b1_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.embeddings.ClusterDistribution</td>\n",
       "      <td id=\"T_3c6b1_row28_col1\" class=\"data row28 col1\" >Cluster Distribution</td>\n",
       "      <td id=\"T_3c6b1_row28_col2\" class=\"data row28 col2\" >Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....</td>\n",
       "      <td id=\"T_3c6b1_row28_col3\" class=\"data row28 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row28_col4\" class=\"data row28 col4\" >{'num_clusters': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.embeddings.EuclideanDistanceHeatmap</td>\n",
       "      <td id=\"T_3c6b1_row29_col1\" class=\"data row29 col1\" >Euclidean Distance Heatmap</td>\n",
       "      <td id=\"T_3c6b1_row29_col2\" class=\"data row29 col2\" >Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....</td>\n",
       "      <td id=\"T_3c6b1_row29_col3\" class=\"data row29 col3\" >['dataset', 'model']</td>\n",
       "      <td id=\"T_3c6b1_row29_col4\" class=\"data row29 col4\" >{'title': 'Euclidean Distance Matrix', 'color': 'Euclidean Distance', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.embeddings.StabilityAnalysis</td>\n",
       "      <td id=\"T_3c6b1_row30_col1\" class=\"data row30 col1\" >Stability Analysis</td>\n",
       "      <td id=\"T_3c6b1_row30_col2\" class=\"data row30 col2\" >Base class for embeddings stability analysis tests</td>\n",
       "      <td id=\"T_3c6b1_row30_col3\" class=\"data row30 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row30_col4\" class=\"data row30 col4\" >{'mean_similarity_threshold': 0.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.embeddings.StabilityAnalysisKeyword</td>\n",
       "      <td id=\"T_3c6b1_row31_col1\" class=\"data row31 col1\" >Stability Analysis Keyword</td>\n",
       "      <td id=\"T_3c6b1_row31_col2\" class=\"data row31 col2\" >Evaluate robustness of embeddings models to keyword swaps on the test dataset...</td>\n",
       "      <td id=\"T_3c6b1_row31_col3\" class=\"data row31 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row31_col4\" class=\"data row31 col4\" >{'keyword_dict': None, 'mean_similarity_threshold': 0.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.embeddings.StabilityAnalysisSynonyms</td>\n",
       "      <td id=\"T_3c6b1_row32_col1\" class=\"data row32 col1\" >Stability Analysis Synonyms</td>\n",
       "      <td id=\"T_3c6b1_row32_col2\" class=\"data row32 col2\" >Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....</td>\n",
       "      <td id=\"T_3c6b1_row32_col3\" class=\"data row32 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row32_col4\" class=\"data row32 col4\" >{'probability': 0.02, 'mean_similarity_threshold': 0.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.embeddings.DescriptiveAnalytics</td>\n",
       "      <td id=\"T_3c6b1_row33_col1\" class=\"data row33 col1\" >Descriptive Analytics</td>\n",
       "      <td id=\"T_3c6b1_row33_col2\" class=\"data row33 col2\" >Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...</td>\n",
       "      <td id=\"T_3c6b1_row33_col3\" class=\"data row33 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row33_col4\" class=\"data row33 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.ragas.ContextEntityRecall</td>\n",
       "      <td id=\"T_3c6b1_row34_col1\" class=\"data row34 col1\" >Context Entity Recall</td>\n",
       "      <td id=\"T_3c6b1_row34_col2\" class=\"data row34 col2\" >Evaluates the context entity recall for dataset entries and visualizes the results....</td>\n",
       "      <td id=\"T_3c6b1_row34_col3\" class=\"data row34 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row34_col4\" class=\"data row34 col4\" >{'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.ragas.Faithfulness</td>\n",
       "      <td id=\"T_3c6b1_row35_col1\" class=\"data row35 col1\" >Faithfulness</td>\n",
       "      <td id=\"T_3c6b1_row35_col2\" class=\"data row35 col2\" >Evaluates the faithfulness of the generated answers with respect to retrieved contexts....</td>\n",
       "      <td id=\"T_3c6b1_row35_col3\" class=\"data row35 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row35_col4\" class=\"data row35 col4\" >{'answer_column': 'answer', 'contexts_column': 'contexts'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.ragas.AspectCritique</td>\n",
       "      <td id=\"T_3c6b1_row36_col1\" class=\"data row36 col1\" >Aspect Critique</td>\n",
       "      <td id=\"T_3c6b1_row36_col2\" class=\"data row36 col2\" >Evaluates generations against the following aspects: harmfulness, maliciousness,...</td>\n",
       "      <td id=\"T_3c6b1_row36_col3\" class=\"data row36 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row36_col4\" class=\"data row36 col4\" >{'question_column': 'question', 'answer_column': 'answer', 'contexts_column': 'contexts', 'aspects': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness'], 'additional_aspects': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.ragas.AnswerSimilarity</td>\n",
       "      <td id=\"T_3c6b1_row37_col1\" class=\"data row37 col1\" >Answer Similarity</td>\n",
       "      <td id=\"T_3c6b1_row37_col2\" class=\"data row37 col2\" >Calculates the semantic similarity between generated answers and ground truths...</td>\n",
       "      <td id=\"T_3c6b1_row37_col3\" class=\"data row37 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row37_col4\" class=\"data row37 col4\" >{'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row38_col0\" class=\"data row38 col0\" >validmind.model_validation.ragas.AnswerCorrectness</td>\n",
       "      <td id=\"T_3c6b1_row38_col1\" class=\"data row38 col1\" >Answer Correctness</td>\n",
       "      <td id=\"T_3c6b1_row38_col2\" class=\"data row38 col2\" >Evaluates the correctness of answers in a dataset with respect to the provided ground...</td>\n",
       "      <td id=\"T_3c6b1_row38_col3\" class=\"data row38 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row38_col4\" class=\"data row38 col4\" >{'question_column': 'question', 'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row39_col0\" class=\"data row39 col0\" >validmind.model_validation.ragas.ContextRecall</td>\n",
       "      <td id=\"T_3c6b1_row39_col1\" class=\"data row39 col1\" >Context Recall</td>\n",
       "      <td id=\"T_3c6b1_row39_col2\" class=\"data row39 col2\" >Context recall measures the extent to which the retrieved context aligns with the...</td>\n",
       "      <td id=\"T_3c6b1_row39_col3\" class=\"data row39 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row39_col4\" class=\"data row39 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row40_col0\" class=\"data row40 col0\" >validmind.model_validation.ragas.ContextRelevancy</td>\n",
       "      <td id=\"T_3c6b1_row40_col1\" class=\"data row40 col1\" >Context Relevancy</td>\n",
       "      <td id=\"T_3c6b1_row40_col2\" class=\"data row40 col2\" >Evaluates the context relevancy metric for entries in a dataset and visualizes the...</td>\n",
       "      <td id=\"T_3c6b1_row40_col3\" class=\"data row40 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row40_col4\" class=\"data row40 col4\" >{'question_column': 'question', 'contexts_column': 'contexts'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row41_col0\" class=\"data row41 col0\" >validmind.model_validation.ragas.ContextPrecision</td>\n",
       "      <td id=\"T_3c6b1_row41_col1\" class=\"data row41 col1\" >Context Precision</td>\n",
       "      <td id=\"T_3c6b1_row41_col2\" class=\"data row41 col2\" >Context Precision is a metric that evaluates whether all of the ground-truth...</td>\n",
       "      <td id=\"T_3c6b1_row41_col3\" class=\"data row41 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row41_col4\" class=\"data row41 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row42_col0\" class=\"data row42 col0\" >validmind.model_validation.ragas.AnswerRelevance</td>\n",
       "      <td id=\"T_3c6b1_row42_col1\" class=\"data row42 col1\" >Answer Relevance</td>\n",
       "      <td id=\"T_3c6b1_row42_col2\" class=\"data row42 col2\" >Assesses how pertinent the generated answer is to the given prompt....</td>\n",
       "      <td id=\"T_3c6b1_row42_col3\" class=\"data row42 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row42_col4\" class=\"data row42 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'answer_column': 'answer'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row43_col0\" class=\"data row43 col0\" >validmind.model_validation.sklearn.RegressionModelsPerformanceComparison</td>\n",
       "      <td id=\"T_3c6b1_row43_col1\" class=\"data row43 col1\" >Regression Models Performance Comparison</td>\n",
       "      <td id=\"T_3c6b1_row43_col2\" class=\"data row43 col2\" >Compares and evaluates the performance of multiple regression models using five different metrics: MAE, MSE, RMSE,...</td>\n",
       "      <td id=\"T_3c6b1_row43_col3\" class=\"data row43 col3\" >['dataset', 'models']</td>\n",
       "      <td id=\"T_3c6b1_row43_col4\" class=\"data row43 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row44_col0\" class=\"data row44 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
       "      <td id=\"T_3c6b1_row44_col1\" class=\"data row44 col1\" >Adjusted Mutual Information</td>\n",
       "      <td id=\"T_3c6b1_row44_col2\" class=\"data row44 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
       "      <td id=\"T_3c6b1_row44_col3\" class=\"data row44 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row44_col4\" class=\"data row44 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row45_col0\" class=\"data row45 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
       "      <td id=\"T_3c6b1_row45_col1\" class=\"data row45 col1\" >Silhouette Plot</td>\n",
       "      <td id=\"T_3c6b1_row45_col2\" class=\"data row45 col2\" >Calculates and visualizes Silhouette Score, assessing degree of data point suitability to its cluster in ML models....</td>\n",
       "      <td id=\"T_3c6b1_row45_col3\" class=\"data row45 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row45_col4\" class=\"data row45 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row46_col0\" class=\"data row46 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
       "      <td id=\"T_3c6b1_row46_col1\" class=\"data row46 col1\" >Robustness Diagnosis</td>\n",
       "      <td id=\"T_3c6b1_row46_col2\" class=\"data row46 col2\" >Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...</td>\n",
       "      <td id=\"T_3c6b1_row46_col3\" class=\"data row46 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row46_col4\" class=\"data row46 col4\" >{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row47_col0\" class=\"data row47 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
       "      <td id=\"T_3c6b1_row47_col1\" class=\"data row47 col1\" >Adjusted Rand Index</td>\n",
       "      <td id=\"T_3c6b1_row47_col2\" class=\"data row47 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
       "      <td id=\"T_3c6b1_row47_col3\" class=\"data row47 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row47_col4\" class=\"data row47 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row48_col0\" class=\"data row48 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
       "      <td id=\"T_3c6b1_row48_col1\" class=\"data row48 col1\" >SHAP Global Importance</td>\n",
       "      <td id=\"T_3c6b1_row48_col2\" class=\"data row48 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
       "      <td id=\"T_3c6b1_row48_col3\" class=\"data row48 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row48_col4\" class=\"data row48 col4\" >{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row49_col0\" class=\"data row49 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
       "      <td id=\"T_3c6b1_row49_col1\" class=\"data row49 col1\" >Confusion Matrix</td>\n",
       "      <td id=\"T_3c6b1_row49_col2\" class=\"data row49 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
       "      <td id=\"T_3c6b1_row49_col3\" class=\"data row49 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row49_col4\" class=\"data row49 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row50_col0\" class=\"data row50 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
       "      <td id=\"T_3c6b1_row50_col1\" class=\"data row50 col1\" >Homogeneity Score</td>\n",
       "      <td id=\"T_3c6b1_row50_col2\" class=\"data row50 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
       "      <td id=\"T_3c6b1_row50_col3\" class=\"data row50 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row50_col4\" class=\"data row50 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row51_col0\" class=\"data row51 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
       "      <td id=\"T_3c6b1_row51_col1\" class=\"data row51 col1\" >Completeness Score</td>\n",
       "      <td id=\"T_3c6b1_row51_col2\" class=\"data row51 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
       "      <td id=\"T_3c6b1_row51_col3\" class=\"data row51 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row51_col4\" class=\"data row51 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row52_col0\" class=\"data row52 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
       "      <td id=\"T_3c6b1_row52_col1\" class=\"data row52 col1\" >Overfit Diagnosis</td>\n",
       "      <td id=\"T_3c6b1_row52_col2\" class=\"data row52 col2\" >Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....</td>\n",
       "      <td id=\"T_3c6b1_row52_col3\" class=\"data row52 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row52_col4\" class=\"data row52 col4\" >{'features_columns': None, 'cut_off_percentage': 4}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row53_col0\" class=\"data row53 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
       "      <td id=\"T_3c6b1_row53_col1\" class=\"data row53 col1\" >Cluster Performance Metrics</td>\n",
       "      <td id=\"T_3c6b1_row53_col2\" class=\"data row53 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
       "      <td id=\"T_3c6b1_row53_col3\" class=\"data row53 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row53_col4\" class=\"data row53 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row54_col0\" class=\"data row54 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
       "      <td id=\"T_3c6b1_row54_col1\" class=\"data row54 col1\" >Permutation Feature Importance</td>\n",
       "      <td id=\"T_3c6b1_row54_col2\" class=\"data row54 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
       "      <td id=\"T_3c6b1_row54_col3\" class=\"data row54 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row54_col4\" class=\"data row54 col4\" >{'fontsize': None, 'figure_height': 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row55_col0\" class=\"data row55 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
       "      <td id=\"T_3c6b1_row55_col1\" class=\"data row55 col1\" >Fowlkes Mallows Score</td>\n",
       "      <td id=\"T_3c6b1_row55_col2\" class=\"data row55 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
       "      <td id=\"T_3c6b1_row55_col3\" class=\"data row55 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row55_col4\" class=\"data row55 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row56_col0\" class=\"data row56 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
       "      <td id=\"T_3c6b1_row56_col1\" class=\"data row56 col1\" >Minimum ROCAUC Score</td>\n",
       "      <td id=\"T_3c6b1_row56_col2\" class=\"data row56 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
       "      <td id=\"T_3c6b1_row56_col3\" class=\"data row56 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row56_col4\" class=\"data row56 col4\" >{'min_threshold': 0.5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row57_col0\" class=\"data row57 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
       "      <td id=\"T_3c6b1_row57_col1\" class=\"data row57 col1\" >Cluster Cosine Similarity</td>\n",
       "      <td id=\"T_3c6b1_row57_col2\" class=\"data row57 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
       "      <td id=\"T_3c6b1_row57_col3\" class=\"data row57 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row57_col4\" class=\"data row57 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row58_col0\" class=\"data row58 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
       "      <td id=\"T_3c6b1_row58_col1\" class=\"data row58 col1\" >Precision Recall Curve</td>\n",
       "      <td id=\"T_3c6b1_row58_col2\" class=\"data row58 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
       "      <td id=\"T_3c6b1_row58_col3\" class=\"data row58 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row58_col4\" class=\"data row58 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row59_col0\" class=\"data row59 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
       "      <td id=\"T_3c6b1_row59_col1\" class=\"data row59 col1\" >Classifier Performance</td>\n",
       "      <td id=\"T_3c6b1_row59_col2\" class=\"data row59 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
       "      <td id=\"T_3c6b1_row59_col3\" class=\"data row59 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row59_col4\" class=\"data row59 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row60_col0\" class=\"data row60 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
       "      <td id=\"T_3c6b1_row60_col1\" class=\"data row60 col1\" >V Measure</td>\n",
       "      <td id=\"T_3c6b1_row60_col2\" class=\"data row60 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
       "      <td id=\"T_3c6b1_row60_col3\" class=\"data row60 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row60_col4\" class=\"data row60 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row61_col0\" class=\"data row61 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
       "      <td id=\"T_3c6b1_row61_col1\" class=\"data row61 col1\" >Minimum F1 Score</td>\n",
       "      <td id=\"T_3c6b1_row61_col2\" class=\"data row61 col2\" >Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....</td>\n",
       "      <td id=\"T_3c6b1_row61_col3\" class=\"data row61 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row61_col4\" class=\"data row61 col4\" >{'min_threshold': 0.5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row62_col0\" class=\"data row62 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
       "      <td id=\"T_3c6b1_row62_col1\" class=\"data row62 col1\" >ROC Curve</td>\n",
       "      <td id=\"T_3c6b1_row62_col2\" class=\"data row62 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
       "      <td id=\"T_3c6b1_row62_col3\" class=\"data row62 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row62_col4\" class=\"data row62 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row63_col0\" class=\"data row63 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
       "      <td id=\"T_3c6b1_row63_col1\" class=\"data row63 col1\" >Regression R2 Square</td>\n",
       "      <td id=\"T_3c6b1_row63_col2\" class=\"data row63 col2\" >**Purpose**: The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a...</td>\n",
       "      <td id=\"T_3c6b1_row63_col3\" class=\"data row63 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row63_col4\" class=\"data row63 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row64_col0\" class=\"data row64 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
       "      <td id=\"T_3c6b1_row64_col1\" class=\"data row64 col1\" >Regression Errors</td>\n",
       "      <td id=\"T_3c6b1_row64_col2\" class=\"data row64 col2\" >**Purpose**: This metric is used to measure the performance of a regression model. It gauges the model's accuracy...</td>\n",
       "      <td id=\"T_3c6b1_row64_col3\" class=\"data row64 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row64_col4\" class=\"data row64 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row65_col0\" class=\"data row65 col0\" >validmind.model_validation.sklearn.ClusterPerformance</td>\n",
       "      <td id=\"T_3c6b1_row65_col1\" class=\"data row65 col1\" >Cluster Performance</td>\n",
       "      <td id=\"T_3c6b1_row65_col2\" class=\"data row65 col2\" >Evaluates and compares a clustering model's performance on training and testing datasets using multiple defined...</td>\n",
       "      <td id=\"T_3c6b1_row65_col3\" class=\"data row65 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row65_col4\" class=\"data row65 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row66_col0\" class=\"data row66 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
       "      <td id=\"T_3c6b1_row66_col1\" class=\"data row66 col1\" >Training Test Degradation</td>\n",
       "      <td id=\"T_3c6b1_row66_col2\" class=\"data row66 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
       "      <td id=\"T_3c6b1_row66_col3\" class=\"data row66 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row66_col4\" class=\"data row66 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row67_col0\" class=\"data row67 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
       "      <td id=\"T_3c6b1_row67_col1\" class=\"data row67 col1\" >Hyper Parameters Tuning</td>\n",
       "      <td id=\"T_3c6b1_row67_col2\" class=\"data row67 col2\" >Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....</td>\n",
       "      <td id=\"T_3c6b1_row67_col3\" class=\"data row67 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row67_col4\" class=\"data row67 col4\" >{'param_grid': None, 'scoring': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row68_col0\" class=\"data row68 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
       "      <td id=\"T_3c6b1_row68_col1\" class=\"data row68 col1\" >K Means Clusters Optimization</td>\n",
       "      <td id=\"T_3c6b1_row68_col2\" class=\"data row68 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
       "      <td id=\"T_3c6b1_row68_col3\" class=\"data row68 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row68_col4\" class=\"data row68 col4\" >{'n_clusters': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row69_col0\" class=\"data row69 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
       "      <td id=\"T_3c6b1_row69_col1\" class=\"data row69 col1\" >Models Performance Comparison</td>\n",
       "      <td id=\"T_3c6b1_row69_col2\" class=\"data row69 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
       "      <td id=\"T_3c6b1_row69_col3\" class=\"data row69 col3\" >['dataset', 'models']</td>\n",
       "      <td id=\"T_3c6b1_row69_col4\" class=\"data row69 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row70_col0\" class=\"data row70 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
       "      <td id=\"T_3c6b1_row70_col1\" class=\"data row70 col1\" >Weakspots Diagnosis</td>\n",
       "      <td id=\"T_3c6b1_row70_col2\" class=\"data row70 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
       "      <td id=\"T_3c6b1_row70_col3\" class=\"data row70 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row70_col4\" class=\"data row70 col4\" >{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row71_col0\" class=\"data row71 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
       "      <td id=\"T_3c6b1_row71_col1\" class=\"data row71 col1\" >Population Stability Index</td>\n",
       "      <td id=\"T_3c6b1_row71_col2\" class=\"data row71 col2\" >Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
       "      <td id=\"T_3c6b1_row71_col3\" class=\"data row71 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row71_col4\" class=\"data row71 col4\" >{'num_bins': 10, 'mode': 'fixed'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row72_col0\" class=\"data row72 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
       "      <td id=\"T_3c6b1_row72_col1\" class=\"data row72 col1\" >Minimum Accuracy</td>\n",
       "      <td id=\"T_3c6b1_row72_col2\" class=\"data row72 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
       "      <td id=\"T_3c6b1_row72_col3\" class=\"data row72 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row72_col4\" class=\"data row72 col4\" >{'min_threshold': 0.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row73_col0\" class=\"data row73 col0\" >validmind.model_validation.statsmodels.RegressionModelsCoeffs</td>\n",
       "      <td id=\"T_3c6b1_row73_col1\" class=\"data row73 col1\" >Regression Models Coeffs</td>\n",
       "      <td id=\"T_3c6b1_row73_col2\" class=\"data row73 col2\" >Compares feature importance by evaluating and contrasting coefficients of different regression models....</td>\n",
       "      <td id=\"T_3c6b1_row73_col3\" class=\"data row73 col3\" >['models']</td>\n",
       "      <td id=\"T_3c6b1_row73_col4\" class=\"data row73 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row74_col0\" class=\"data row74 col0\" >validmind.model_validation.statsmodels.BoxPierce</td>\n",
       "      <td id=\"T_3c6b1_row74_col1\" class=\"data row74 col1\" >Box Pierce</td>\n",
       "      <td id=\"T_3c6b1_row74_col2\" class=\"data row74 col2\" >Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....</td>\n",
       "      <td id=\"T_3c6b1_row74_col3\" class=\"data row74 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row74_col4\" class=\"data row74 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row75_col0\" class=\"data row75 col0\" >validmind.model_validation.statsmodels.RegressionCoeffsPlot</td>\n",
       "      <td id=\"T_3c6b1_row75_col1\" class=\"data row75 col1\" >Regression Coeffs Plot</td>\n",
       "      <td id=\"T_3c6b1_row75_col2\" class=\"data row75 col2\" >Visualizes regression coefficients with 95% confidence intervals to assess predictor variables' impact on response...</td>\n",
       "      <td id=\"T_3c6b1_row75_col3\" class=\"data row75 col3\" >['models']</td>\n",
       "      <td id=\"T_3c6b1_row75_col4\" class=\"data row75 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row76_col0\" class=\"data row76 col0\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
       "      <td id=\"T_3c6b1_row76_col1\" class=\"data row76 col1\" >Regression Model Sensitivity Plot</td>\n",
       "      <td id=\"T_3c6b1_row76_col2\" class=\"data row76 col2\" >Tests the sensitivity of a regression model to variations in independent variables by applying shocks and...</td>\n",
       "      <td id=\"T_3c6b1_row76_col3\" class=\"data row76 col3\" >['models', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row76_col4\" class=\"data row76 col4\" >{'transformation': None, 'shocks': [0.1]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row77_col0\" class=\"data row77 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels</td>\n",
       "      <td id=\"T_3c6b1_row77_col1\" class=\"data row77 col1\" >Regression Model Forecast Plot Levels</td>\n",
       "      <td id=\"T_3c6b1_row77_col2\" class=\"data row77 col2\" >Compares and visualizes forecasted and actual values of regression models on both raw and transformed datasets....</td>\n",
       "      <td id=\"T_3c6b1_row77_col3\" class=\"data row77 col3\" >['models', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row77_col4\" class=\"data row77 col4\" >{'transformation': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row78_col0\" class=\"data row78 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
       "      <td id=\"T_3c6b1_row78_col1\" class=\"data row78 col1\" >Scorecard Histogram</td>\n",
       "      <td id=\"T_3c6b1_row78_col2\" class=\"data row78 col2\" >Creates histograms of credit scores, from both default and non-default instances, generated by a credit-risk model....</td>\n",
       "      <td id=\"T_3c6b1_row78_col3\" class=\"data row78 col3\" >['datasets']</td>\n",
       "      <td id=\"T_3c6b1_row78_col4\" class=\"data row78 col4\" >{'title': 'Histogram of Scores', 'score_column': 'score'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row79_col0\" class=\"data row79 col0\" >validmind.model_validation.statsmodels.LJungBox</td>\n",
       "      <td id=\"T_3c6b1_row79_col1\" class=\"data row79 col1\" >L Jung Box</td>\n",
       "      <td id=\"T_3c6b1_row79_col2\" class=\"data row79 col2\" >Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....</td>\n",
       "      <td id=\"T_3c6b1_row79_col3\" class=\"data row79 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row79_col4\" class=\"data row79 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row80_col0\" class=\"data row80 col0\" >validmind.model_validation.statsmodels.JarqueBera</td>\n",
       "      <td id=\"T_3c6b1_row80_col1\" class=\"data row80 col1\" >Jarque Bera</td>\n",
       "      <td id=\"T_3c6b1_row80_col2\" class=\"data row80 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
       "      <td id=\"T_3c6b1_row80_col3\" class=\"data row80 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row80_col4\" class=\"data row80 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row81_col0\" class=\"data row81 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
       "      <td id=\"T_3c6b1_row81_col1\" class=\"data row81 col1\" >Kolmogorov Smirnov</td>\n",
       "      <td id=\"T_3c6b1_row81_col2\" class=\"data row81 col2\" >Executes a feature-wise Kolmogorov-Smirnov test to evaluate alignment with normal distribution in datasets....</td>\n",
       "      <td id=\"T_3c6b1_row81_col3\" class=\"data row81 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row81_col4\" class=\"data row81 col4\" >{'dist': 'norm'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row82_col0\" class=\"data row82 col0\" >validmind.model_validation.statsmodels.ShapiroWilk</td>\n",
       "      <td id=\"T_3c6b1_row82_col1\" class=\"data row82 col1\" >Shapiro Wilk</td>\n",
       "      <td id=\"T_3c6b1_row82_col2\" class=\"data row82 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
       "      <td id=\"T_3c6b1_row82_col3\" class=\"data row82 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row82_col4\" class=\"data row82 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row83_col0\" class=\"data row83 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
       "      <td id=\"T_3c6b1_row83_col1\" class=\"data row83 col1\" >Cumulative Prediction Probabilities</td>\n",
       "      <td id=\"T_3c6b1_row83_col2\" class=\"data row83 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in logistic...</td>\n",
       "      <td id=\"T_3c6b1_row83_col3\" class=\"data row83 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row83_col4\" class=\"data row83 col4\" >{'title': 'Cumulative Probabilities'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row84_col0\" class=\"data row84 col0\" >validmind.model_validation.statsmodels.RegressionFeatureSignificance</td>\n",
       "      <td id=\"T_3c6b1_row84_col1\" class=\"data row84 col1\" >Regression Feature Significance</td>\n",
       "      <td id=\"T_3c6b1_row84_col2\" class=\"data row84 col2\" >Assesses and visualizes the statistical significance of features in a set of regression models....</td>\n",
       "      <td id=\"T_3c6b1_row84_col3\" class=\"data row84 col3\" >['models']</td>\n",
       "      <td id=\"T_3c6b1_row84_col4\" class=\"data row84 col4\" >{'fontsize': 10, 'p_threshold': 0.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row85_col0\" class=\"data row85 col0\" >validmind.model_validation.statsmodels.RegressionModelSummary</td>\n",
       "      <td id=\"T_3c6b1_row85_col1\" class=\"data row85 col1\" >Regression Model Summary</td>\n",
       "      <td id=\"T_3c6b1_row85_col2\" class=\"data row85 col2\" >Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....</td>\n",
       "      <td id=\"T_3c6b1_row85_col3\" class=\"data row85 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row85_col4\" class=\"data row85 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row86_col0\" class=\"data row86 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
       "      <td id=\"T_3c6b1_row86_col1\" class=\"data row86 col1\" >Lilliefors</td>\n",
       "      <td id=\"T_3c6b1_row86_col2\" class=\"data row86 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
       "      <td id=\"T_3c6b1_row86_col3\" class=\"data row86 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row86_col4\" class=\"data row86 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row87_col0\" class=\"data row87 col0\" >validmind.model_validation.statsmodels.RunsTest</td>\n",
       "      <td id=\"T_3c6b1_row87_col1\" class=\"data row87 col1\" >Runs Test</td>\n",
       "      <td id=\"T_3c6b1_row87_col2\" class=\"data row87 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
       "      <td id=\"T_3c6b1_row87_col3\" class=\"data row87 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row87_col4\" class=\"data row87 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row88_col0\" class=\"data row88 col0\" >validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance</td>\n",
       "      <td id=\"T_3c6b1_row88_col1\" class=\"data row88 col1\" >Regression Permutation Feature Importance</td>\n",
       "      <td id=\"T_3c6b1_row88_col2\" class=\"data row88 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
       "      <td id=\"T_3c6b1_row88_col3\" class=\"data row88 col3\" >['model', 'dataset']</td>\n",
       "      <td id=\"T_3c6b1_row88_col4\" class=\"data row88 col4\" >{'fontsize': 12, 'figure_height': 500}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row89_col0\" class=\"data row89 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
       "      <td id=\"T_3c6b1_row89_col1\" class=\"data row89 col1\" >Prediction Probabilities Histogram</td>\n",
       "      <td id=\"T_3c6b1_row89_col2\" class=\"data row89 col2\" >Generates and visualizes histograms of the Probability of Default predictions for both positive and negative...</td>\n",
       "      <td id=\"T_3c6b1_row89_col3\" class=\"data row89 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row89_col4\" class=\"data row89 col4\" >{'title': 'Histogram of Predictive Probabilities'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row90_col0\" class=\"data row90 col0\" >validmind.model_validation.statsmodels.AutoARIMA</td>\n",
       "      <td id=\"T_3c6b1_row90_col1\" class=\"data row90 col1\" >Auto ARIMA</td>\n",
       "      <td id=\"T_3c6b1_row90_col2\" class=\"data row90 col2\" >Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....</td>\n",
       "      <td id=\"T_3c6b1_row90_col3\" class=\"data row90 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row90_col4\" class=\"data row90 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row91_col0\" class=\"data row91 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
       "      <td id=\"T_3c6b1_row91_col1\" class=\"data row91 col1\" >GINI Table</td>\n",
       "      <td id=\"T_3c6b1_row91_col2\" class=\"data row91 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
       "      <td id=\"T_3c6b1_row91_col3\" class=\"data row91 col3\" >['model', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row91_col4\" class=\"data row91 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row92_col0\" class=\"data row92 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlot</td>\n",
       "      <td id=\"T_3c6b1_row92_col1\" class=\"data row92 col1\" >Regression Model Forecast Plot</td>\n",
       "      <td id=\"T_3c6b1_row92_col2\" class=\"data row92 col2\" >Generates plots to visually compare the forecasted outcomes of one or more regression models against actual...</td>\n",
       "      <td id=\"T_3c6b1_row92_col3\" class=\"data row92 col3\" >['models', 'datasets']</td>\n",
       "      <td id=\"T_3c6b1_row92_col4\" class=\"data row92 col4\" >{'start_date': None, 'end_date': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row93_col0\" class=\"data row93 col0\" >validmind.model_validation.statsmodels.DurbinWatsonTest</td>\n",
       "      <td id=\"T_3c6b1_row93_col1\" class=\"data row93 col1\" >Durbin Watson Test</td>\n",
       "      <td id=\"T_3c6b1_row93_col2\" class=\"data row93 col2\" >Assesses autocorrelation in time series data features using the Durbin-Watson statistic....</td>\n",
       "      <td id=\"T_3c6b1_row93_col3\" class=\"data row93 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row93_col4\" class=\"data row93 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row94_col0\" class=\"data row94 col0\" >validmind.data_validation.MissingValuesRisk</td>\n",
       "      <td id=\"T_3c6b1_row94_col1\" class=\"data row94 col1\" >Missing Values Risk</td>\n",
       "      <td id=\"T_3c6b1_row94_col2\" class=\"data row94 col2\" >Assesses and quantifies the risk related to missing values in a dataset used for training an ML model....</td>\n",
       "      <td id=\"T_3c6b1_row94_col3\" class=\"data row94 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row94_col4\" class=\"data row94 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row95_col0\" class=\"data row95 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
       "      <td id=\"T_3c6b1_row95_col1\" class=\"data row95 col1\" >IQR Outliers Table</td>\n",
       "      <td id=\"T_3c6b1_row95_col2\" class=\"data row95 col2\" >Determines and summarizes outliers in numerical features using Interquartile Range method....</td>\n",
       "      <td id=\"T_3c6b1_row95_col3\" class=\"data row95 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row95_col4\" class=\"data row95 col4\" >{'features': None, 'threshold': 1.5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row96_col0\" class=\"data row96 col0\" >validmind.data_validation.BivariateFeaturesBarPlots</td>\n",
       "      <td id=\"T_3c6b1_row96_col1\" class=\"data row96 col1\" >Bivariate Features Bar Plots</td>\n",
       "      <td id=\"T_3c6b1_row96_col2\" class=\"data row96 col2\" >Generates visual bar plots to analyze the relationship between paired features within categorical data in the model....</td>\n",
       "      <td id=\"T_3c6b1_row96_col3\" class=\"data row96 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row96_col4\" class=\"data row96 col4\" >{'features_pairs': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row97_col0\" class=\"data row97 col0\" >validmind.data_validation.Skewness</td>\n",
       "      <td id=\"T_3c6b1_row97_col1\" class=\"data row97 col1\" >Skewness</td>\n",
       "      <td id=\"T_3c6b1_row97_col2\" class=\"data row97 col2\" >Evaluates the skewness of numerical data in a machine learning model and checks if it falls below a set maximum...</td>\n",
       "      <td id=\"T_3c6b1_row97_col3\" class=\"data row97 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row97_col4\" class=\"data row97 col4\" >{'max_threshold': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row98_col0\" class=\"data row98 col0\" >validmind.data_validation.Duplicates</td>\n",
       "      <td id=\"T_3c6b1_row98_col1\" class=\"data row98 col1\" >Duplicates</td>\n",
       "      <td id=\"T_3c6b1_row98_col2\" class=\"data row98 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
       "      <td id=\"T_3c6b1_row98_col3\" class=\"data row98 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row98_col4\" class=\"data row98 col4\" >{'min_threshold': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row99_col0\" class=\"data row99 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
       "      <td id=\"T_3c6b1_row99_col1\" class=\"data row99 col1\" >Missing Values Bar Plot</td>\n",
       "      <td id=\"T_3c6b1_row99_col2\" class=\"data row99 col2\" >Creates a bar plot showcasing the percentage of missing values in each column of the dataset with risk...</td>\n",
       "      <td id=\"T_3c6b1_row99_col3\" class=\"data row99 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row99_col4\" class=\"data row99 col4\" >{'threshold': 80, 'fig_height': 600}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row100_col0\" class=\"data row100 col0\" >validmind.data_validation.DatasetDescription</td>\n",
       "      <td id=\"T_3c6b1_row100_col1\" class=\"data row100 col1\" >Dataset Description</td>\n",
       "      <td id=\"T_3c6b1_row100_col2\" class=\"data row100 col2\" >Provides comprehensive analysis and statistical summaries of each field in a machine learning model's dataset....</td>\n",
       "      <td id=\"T_3c6b1_row100_col3\" class=\"data row100 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row100_col4\" class=\"data row100 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row101_col0\" class=\"data row101 col0\" >validmind.data_validation.ZivotAndrewsArch</td>\n",
       "      <td id=\"T_3c6b1_row101_col1\" class=\"data row101 col1\" >Zivot Andrews Arch</td>\n",
       "      <td id=\"T_3c6b1_row101_col2\" class=\"data row101 col2\" >Evaluates the order of integration and stationarity of time series data using Zivot-Andrews unit root test....</td>\n",
       "      <td id=\"T_3c6b1_row101_col3\" class=\"data row101 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row101_col4\" class=\"data row101 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row102_col0\" class=\"data row102 col0\" >validmind.data_validation.ScatterPlot</td>\n",
       "      <td id=\"T_3c6b1_row102_col1\" class=\"data row102 col1\" >Scatter Plot</td>\n",
       "      <td id=\"T_3c6b1_row102_col2\" class=\"data row102 col2\" >Creates a scatter plot matrix to visually analyze feature relationships, patterns, and outliers in a dataset....</td>\n",
       "      <td id=\"T_3c6b1_row102_col3\" class=\"data row102 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row102_col4\" class=\"data row102 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row103_col0\" class=\"data row103 col0\" >validmind.data_validation.TimeSeriesOutliers</td>\n",
       "      <td id=\"T_3c6b1_row103_col1\" class=\"data row103 col1\" >Time Series Outliers</td>\n",
       "      <td id=\"T_3c6b1_row103_col2\" class=\"data row103 col2\" >Identifies and visualizes outliers in time-series data using z-score method....</td>\n",
       "      <td id=\"T_3c6b1_row103_col3\" class=\"data row103 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row103_col4\" class=\"data row103 col4\" >{'zscore_threshold': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row104_col0\" class=\"data row104 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
       "      <td id=\"T_3c6b1_row104_col1\" class=\"data row104 col1\" >Tabular Categorical Bar Plots</td>\n",
       "      <td id=\"T_3c6b1_row104_col2\" class=\"data row104 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate dataset's composition....</td>\n",
       "      <td id=\"T_3c6b1_row104_col3\" class=\"data row104 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row104_col4\" class=\"data row104 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row105_col0\" class=\"data row105 col0\" >validmind.data_validation.AutoStationarity</td>\n",
       "      <td id=\"T_3c6b1_row105_col1\" class=\"data row105 col1\" >Auto Stationarity</td>\n",
       "      <td id=\"T_3c6b1_row105_col2\" class=\"data row105 col2\" >Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....</td>\n",
       "      <td id=\"T_3c6b1_row105_col3\" class=\"data row105 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row105_col4\" class=\"data row105 col4\" >{'max_order': 5, 'threshold': 0.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row106_col0\" class=\"data row106 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
       "      <td id=\"T_3c6b1_row106_col1\" class=\"data row106 col1\" >Descriptive Statistics</td>\n",
       "      <td id=\"T_3c6b1_row106_col2\" class=\"data row106 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
       "      <td id=\"T_3c6b1_row106_col3\" class=\"data row106 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row106_col4\" class=\"data row106 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row107_col0\" class=\"data row107 col0\" >validmind.data_validation.ANOVAOneWayTable</td>\n",
       "      <td id=\"T_3c6b1_row107_col1\" class=\"data row107 col1\" >ANOVA One Way Table</td>\n",
       "      <td id=\"T_3c6b1_row107_col2\" class=\"data row107 col2\" >Applies one-way ANOVA (Analysis of Variance) to identify statistically significant numerical features in the...</td>\n",
       "      <td id=\"T_3c6b1_row107_col3\" class=\"data row107 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row107_col4\" class=\"data row107 col4\" >{'features': None, 'p_threshold': 0.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row108_col0\" class=\"data row108 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
       "      <td id=\"T_3c6b1_row108_col1\" class=\"data row108 col1\" >Target Rate Bar Plots</td>\n",
       "      <td id=\"T_3c6b1_row108_col2\" class=\"data row108 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
       "      <td id=\"T_3c6b1_row108_col3\" class=\"data row108 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row108_col4\" class=\"data row108 col4\" >{'default_column': None, 'columns': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row109_col0\" class=\"data row109 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
       "      <td id=\"T_3c6b1_row109_col1\" class=\"data row109 col1\" >Pearson Correlation Matrix</td>\n",
       "      <td id=\"T_3c6b1_row109_col2\" class=\"data row109 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
       "      <td id=\"T_3c6b1_row109_col3\" class=\"data row109 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row109_col4\" class=\"data row109 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row110_col0\" class=\"data row110 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
       "      <td id=\"T_3c6b1_row110_col1\" class=\"data row110 col1\" >Feature Target Correlation Plot</td>\n",
       "      <td id=\"T_3c6b1_row110_col2\" class=\"data row110 col2\" >Visualizes the correlation between input features and model's target output in a color-coded horizontal bar plot....</td>\n",
       "      <td id=\"T_3c6b1_row110_col3\" class=\"data row110 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row110_col4\" class=\"data row110 col4\" >{'features': None, 'fig_height': 600}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row111_col0\" class=\"data row111 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
       "      <td id=\"T_3c6b1_row111_col1\" class=\"data row111 col1\" >Tabular Numerical Histograms</td>\n",
       "      <td id=\"T_3c6b1_row111_col2\" class=\"data row111 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
       "      <td id=\"T_3c6b1_row111_col3\" class=\"data row111 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row111_col4\" class=\"data row111 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row112_col0\" class=\"data row112 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
       "      <td id=\"T_3c6b1_row112_col1\" class=\"data row112 col1\" >Isolation Forest Outliers</td>\n",
       "      <td id=\"T_3c6b1_row112_col2\" class=\"data row112 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
       "      <td id=\"T_3c6b1_row112_col3\" class=\"data row112 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row112_col4\" class=\"data row112 col4\" >{'random_state': 0, 'contamination': 0.1, 'features_columns': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row113_col0\" class=\"data row113 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
       "      <td id=\"T_3c6b1_row113_col1\" class=\"data row113 col1\" >Chi Squared Features Table</td>\n",
       "      <td id=\"T_3c6b1_row113_col2\" class=\"data row113 col2\" >Executes Chi-Squared test for each categorical feature against a target column to assess significant association....</td>\n",
       "      <td id=\"T_3c6b1_row113_col3\" class=\"data row113 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row113_col4\" class=\"data row113 col4\" >{'cat_features': None, 'p_threshold': 0.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row114_col0\" class=\"data row114 col0\" >validmind.data_validation.HighCardinality</td>\n",
       "      <td id=\"T_3c6b1_row114_col1\" class=\"data row114 col1\" >High Cardinality</td>\n",
       "      <td id=\"T_3c6b1_row114_col2\" class=\"data row114 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
       "      <td id=\"T_3c6b1_row114_col3\" class=\"data row114 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row114_col4\" class=\"data row114 col4\" >{'num_threshold': 100, 'percent_threshold': 0.1, 'threshold_type': 'percent'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row115_col0\" class=\"data row115 col0\" >validmind.data_validation.MissingValues</td>\n",
       "      <td id=\"T_3c6b1_row115_col1\" class=\"data row115 col1\" >Missing Values</td>\n",
       "      <td id=\"T_3c6b1_row115_col2\" class=\"data row115 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
       "      <td id=\"T_3c6b1_row115_col3\" class=\"data row115 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row115_col4\" class=\"data row115 col4\" >{'min_threshold': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row116_col0\" class=\"data row116 col0\" >validmind.data_validation.PhillipsPerronArch</td>\n",
       "      <td id=\"T_3c6b1_row116_col1\" class=\"data row116 col1\" >Phillips Perron Arch</td>\n",
       "      <td id=\"T_3c6b1_row116_col2\" class=\"data row116 col2\" >Executes Phillips-Perron test to assess the stationarity of time series data in each ML model feature....</td>\n",
       "      <td id=\"T_3c6b1_row116_col3\" class=\"data row116 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row116_col4\" class=\"data row116 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row117_col0\" class=\"data row117 col0\" >validmind.data_validation.RollingStatsPlot</td>\n",
       "      <td id=\"T_3c6b1_row117_col1\" class=\"data row117 col1\" >Rolling Stats Plot</td>\n",
       "      <td id=\"T_3c6b1_row117_col2\" class=\"data row117 col2\" >This test evaluates the stationarity of time series data by plotting its rolling mean and standard deviation....</td>\n",
       "      <td id=\"T_3c6b1_row117_col3\" class=\"data row117 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row117_col4\" class=\"data row117 col4\" >{'window_size': 12}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row118_col0\" class=\"data row118 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
       "      <td id=\"T_3c6b1_row118_col1\" class=\"data row118 col1\" >Tabular Description Tables</td>\n",
       "      <td id=\"T_3c6b1_row118_col2\" class=\"data row118 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
       "      <td id=\"T_3c6b1_row118_col3\" class=\"data row118 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row118_col4\" class=\"data row118 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row119_col0\" class=\"data row119 col0\" >validmind.data_validation.AutoMA</td>\n",
       "      <td id=\"T_3c6b1_row119_col1\" class=\"data row119 col1\" >Auto MA</td>\n",
       "      <td id=\"T_3c6b1_row119_col2\" class=\"data row119 col2\" >Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...</td>\n",
       "      <td id=\"T_3c6b1_row119_col3\" class=\"data row119 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row119_col4\" class=\"data row119 col4\" >{'max_ma_order': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row120_col0\" class=\"data row120 col0\" >validmind.data_validation.UniqueRows</td>\n",
       "      <td id=\"T_3c6b1_row120_col1\" class=\"data row120 col1\" >Unique Rows</td>\n",
       "      <td id=\"T_3c6b1_row120_col2\" class=\"data row120 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
       "      <td id=\"T_3c6b1_row120_col3\" class=\"data row120 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row120_col4\" class=\"data row120 col4\" >{'min_percent_threshold': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row121_col0\" class=\"data row121 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
       "      <td id=\"T_3c6b1_row121_col1\" class=\"data row121 col1\" >Too Many Zero Values</td>\n",
       "      <td id=\"T_3c6b1_row121_col2\" class=\"data row121 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
       "      <td id=\"T_3c6b1_row121_col3\" class=\"data row121 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row121_col4\" class=\"data row121 col4\" >{'max_percent_threshold': 0.03}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row122_col0\" class=\"data row122 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
       "      <td id=\"T_3c6b1_row122_col1\" class=\"data row122 col1\" >High Pearson Correlation</td>\n",
       "      <td id=\"T_3c6b1_row122_col2\" class=\"data row122 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
       "      <td id=\"T_3c6b1_row122_col3\" class=\"data row122 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row122_col4\" class=\"data row122 col4\" >{'max_threshold': 0.3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row123_col0\" class=\"data row123 col0\" >validmind.data_validation.ACFandPACFPlot</td>\n",
       "      <td id=\"T_3c6b1_row123_col1\" class=\"data row123 col1\" >AC Fand PACF Plot</td>\n",
       "      <td id=\"T_3c6b1_row123_col2\" class=\"data row123 col2\" >Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...</td>\n",
       "      <td id=\"T_3c6b1_row123_col3\" class=\"data row123 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row123_col4\" class=\"data row123 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row124_col0\" class=\"data row124 col0\" >validmind.data_validation.BivariateHistograms</td>\n",
       "      <td id=\"T_3c6b1_row124_col1\" class=\"data row124 col1\" >Bivariate Histograms</td>\n",
       "      <td id=\"T_3c6b1_row124_col2\" class=\"data row124 col2\" >Generates bivariate histograms for paired features, aiding in visual inspection of categorical variables'...</td>\n",
       "      <td id=\"T_3c6b1_row124_col3\" class=\"data row124 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row124_col4\" class=\"data row124 col4\" >{'features_pairs': None, 'target_filter': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row125_col0\" class=\"data row125 col0\" >validmind.data_validation.WOEBinTable</td>\n",
       "      <td id=\"T_3c6b1_row125_col1\" class=\"data row125 col1\" >WOE Bin Table</td>\n",
       "      <td id=\"T_3c6b1_row125_col2\" class=\"data row125 col2\" >Calculates and assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature in a ML model....</td>\n",
       "      <td id=\"T_3c6b1_row125_col3\" class=\"data row125 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row125_col4\" class=\"data row125 col4\" >{'breaks_adj': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row126_col0\" class=\"data row126 col0\" >validmind.data_validation.HeatmapFeatureCorrelations</td>\n",
       "      <td id=\"T_3c6b1_row126_col1\" class=\"data row126 col1\" >Heatmap Feature Correlations</td>\n",
       "      <td id=\"T_3c6b1_row126_col2\" class=\"data row126 col2\" >Creates a heatmap to visually represent correlation patterns between pairs of numerical features in a dataset....</td>\n",
       "      <td id=\"T_3c6b1_row126_col3\" class=\"data row126 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row126_col4\" class=\"data row126 col4\" >{'declutter': None, 'fontsize': None, 'num_features': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row127_col0\" class=\"data row127 col0\" >validmind.data_validation.TimeSeriesFrequency</td>\n",
       "      <td id=\"T_3c6b1_row127_col1\" class=\"data row127 col1\" >Time Series Frequency</td>\n",
       "      <td id=\"T_3c6b1_row127_col2\" class=\"data row127 col2\" >Evaluates consistency of time series data frequency and generates a frequency plot....</td>\n",
       "      <td id=\"T_3c6b1_row127_col3\" class=\"data row127 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row127_col4\" class=\"data row127 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row128_col0\" class=\"data row128 col0\" >validmind.data_validation.DatasetSplit</td>\n",
       "      <td id=\"T_3c6b1_row128_col1\" class=\"data row128 col1\" >Dataset Split</td>\n",
       "      <td id=\"T_3c6b1_row128_col2\" class=\"data row128 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
       "      <td id=\"T_3c6b1_row128_col3\" class=\"data row128 col3\" >['datasets']</td>\n",
       "      <td id=\"T_3c6b1_row128_col4\" class=\"data row128 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row129_col0\" class=\"data row129 col0\" >validmind.data_validation.SpreadPlot</td>\n",
       "      <td id=\"T_3c6b1_row129_col1\" class=\"data row129 col1\" >Spread Plot</td>\n",
       "      <td id=\"T_3c6b1_row129_col2\" class=\"data row129 col2\" >Visualizes the spread relationship between pairs of time-series variables in a dataset, thereby aiding in...</td>\n",
       "      <td id=\"T_3c6b1_row129_col3\" class=\"data row129 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row129_col4\" class=\"data row129 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row130_col0\" class=\"data row130 col0\" >validmind.data_validation.TimeSeriesLinePlot</td>\n",
       "      <td id=\"T_3c6b1_row130_col1\" class=\"data row130 col1\" >Time Series Line Plot</td>\n",
       "      <td id=\"T_3c6b1_row130_col2\" class=\"data row130 col2\" >Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....</td>\n",
       "      <td id=\"T_3c6b1_row130_col3\" class=\"data row130 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row130_col4\" class=\"data row130 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row131_col0\" class=\"data row131 col0\" >validmind.data_validation.KPSS</td>\n",
       "      <td id=\"T_3c6b1_row131_col1\" class=\"data row131 col1\" >KPSS</td>\n",
       "      <td id=\"T_3c6b1_row131_col2\" class=\"data row131 col2\" >Executes KPSS unit root test to validate stationarity of time-series data in machine learning model....</td>\n",
       "      <td id=\"T_3c6b1_row131_col3\" class=\"data row131 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row131_col4\" class=\"data row131 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row132_col0\" class=\"data row132 col0\" >validmind.data_validation.AutoSeasonality</td>\n",
       "      <td id=\"T_3c6b1_row132_col1\" class=\"data row132 col1\" >Auto Seasonality</td>\n",
       "      <td id=\"T_3c6b1_row132_col2\" class=\"data row132 col2\" >Automatically identifies and quantifies optimal seasonality in time series data to improve forecasting model...</td>\n",
       "      <td id=\"T_3c6b1_row132_col3\" class=\"data row132 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row132_col4\" class=\"data row132 col4\" >{'min_period': 1, 'max_period': 4}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row133_col0\" class=\"data row133 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
       "      <td id=\"T_3c6b1_row133_col1\" class=\"data row133 col1\" >Bivariate Scatter Plots</td>\n",
       "      <td id=\"T_3c6b1_row133_col2\" class=\"data row133 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of predictor variables in machine...</td>\n",
       "      <td id=\"T_3c6b1_row133_col3\" class=\"data row133 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row133_col4\" class=\"data row133 col4\" >{'selected_columns': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row134_col0\" class=\"data row134 col0\" >validmind.data_validation.EngleGrangerCoint</td>\n",
       "      <td id=\"T_3c6b1_row134_col1\" class=\"data row134 col1\" >Engle Granger Coint</td>\n",
       "      <td id=\"T_3c6b1_row134_col2\" class=\"data row134 col2\" >Validates co-integration in pairs of time series data using the Engle-Granger test and classifies them as...</td>\n",
       "      <td id=\"T_3c6b1_row134_col3\" class=\"data row134 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row134_col4\" class=\"data row134 col4\" >{'threshold': 0.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row135_col0\" class=\"data row135 col0\" >validmind.data_validation.TimeSeriesMissingValues</td>\n",
       "      <td id=\"T_3c6b1_row135_col1\" class=\"data row135 col1\" >Time Series Missing Values</td>\n",
       "      <td id=\"T_3c6b1_row135_col2\" class=\"data row135 col2\" >Validates time-series data quality by confirming the count of missing values is below a certain threshold....</td>\n",
       "      <td id=\"T_3c6b1_row135_col3\" class=\"data row135 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row135_col4\" class=\"data row135 col4\" >{'min_threshold': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row136_col0\" class=\"data row136 col0\" >validmind.data_validation.TimeSeriesHistogram</td>\n",
       "      <td id=\"T_3c6b1_row136_col1\" class=\"data row136 col1\" >Time Series Histogram</td>\n",
       "      <td id=\"T_3c6b1_row136_col2\" class=\"data row136 col2\" >Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....</td>\n",
       "      <td id=\"T_3c6b1_row136_col3\" class=\"data row136 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row136_col4\" class=\"data row136 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row137_col0\" class=\"data row137 col0\" >validmind.data_validation.LaggedCorrelationHeatmap</td>\n",
       "      <td id=\"T_3c6b1_row137_col1\" class=\"data row137 col1\" >Lagged Correlation Heatmap</td>\n",
       "      <td id=\"T_3c6b1_row137_col2\" class=\"data row137 col2\" >Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...</td>\n",
       "      <td id=\"T_3c6b1_row137_col3\" class=\"data row137 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row137_col4\" class=\"data row137 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row138_col0\" class=\"data row138 col0\" >validmind.data_validation.SeasonalDecompose</td>\n",
       "      <td id=\"T_3c6b1_row138_col1\" class=\"data row138 col1\" >Seasonal Decompose</td>\n",
       "      <td id=\"T_3c6b1_row138_col2\" class=\"data row138 col2\" >Decomposes dataset features into observed, trend, seasonal, and residual components to identify patterns and...</td>\n",
       "      <td id=\"T_3c6b1_row138_col3\" class=\"data row138 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row138_col4\" class=\"data row138 col4\" >{'seasonal_model': 'additive'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row139_col0\" class=\"data row139 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
       "      <td id=\"T_3c6b1_row139_col1\" class=\"data row139 col1\" >WOE Bin Plots</td>\n",
       "      <td id=\"T_3c6b1_row139_col2\" class=\"data row139 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
       "      <td id=\"T_3c6b1_row139_col3\" class=\"data row139 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row139_col4\" class=\"data row139 col4\" >{'breaks_adj': None, 'fig_height': 600, 'fig_width': 500}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row140_col0\" class=\"data row140 col0\" >validmind.data_validation.ClassImbalance</td>\n",
       "      <td id=\"T_3c6b1_row140_col1\" class=\"data row140 col1\" >Class Imbalance</td>\n",
       "      <td id=\"T_3c6b1_row140_col2\" class=\"data row140 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
       "      <td id=\"T_3c6b1_row140_col3\" class=\"data row140 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row140_col4\" class=\"data row140 col4\" >{'min_percent_threshold': 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row141_col0\" class=\"data row141 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
       "      <td id=\"T_3c6b1_row141_col1\" class=\"data row141 col1\" >IQR Outliers Bar Plot</td>\n",
       "      <td id=\"T_3c6b1_row141_col2\" class=\"data row141 col2\" >Visualizes outlier distribution across percentiles in numerical data using Interquartile Range (IQR) method....</td>\n",
       "      <td id=\"T_3c6b1_row141_col3\" class=\"data row141 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row141_col4\" class=\"data row141 col4\" >{'threshold': 1.5, 'num_features': None, 'fig_width': 800}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row142_col0\" class=\"data row142 col0\" >validmind.data_validation.DFGLSArch</td>\n",
       "      <td id=\"T_3c6b1_row142_col1\" class=\"data row142 col1\" >DFGLS Arch</td>\n",
       "      <td id=\"T_3c6b1_row142_col2\" class=\"data row142 col2\" >Executes Dickey-Fuller GLS metric to determine order of integration and check stationarity in time series data....</td>\n",
       "      <td id=\"T_3c6b1_row142_col3\" class=\"data row142 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row142_col4\" class=\"data row142 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row143_col0\" class=\"data row143 col0\" >validmind.data_validation.AutoAR</td>\n",
       "      <td id=\"T_3c6b1_row143_col1\" class=\"data row143 col1\" >Auto AR</td>\n",
       "      <td id=\"T_3c6b1_row143_col2\" class=\"data row143 col2\" >Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....</td>\n",
       "      <td id=\"T_3c6b1_row143_col3\" class=\"data row143 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row143_col4\" class=\"data row143 col4\" >{'max_ar_order': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row144_col0\" class=\"data row144 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
       "      <td id=\"T_3c6b1_row144_col1\" class=\"data row144 col1\" >Tabular Date Time Histograms</td>\n",
       "      <td id=\"T_3c6b1_row144_col2\" class=\"data row144 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in model's datetime data....</td>\n",
       "      <td id=\"T_3c6b1_row144_col3\" class=\"data row144 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row144_col4\" class=\"data row144 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row145_col0\" class=\"data row145 col0\" >validmind.data_validation.ADF</td>\n",
       "      <td id=\"T_3c6b1_row145_col1\" class=\"data row145 col1\" >ADF</td>\n",
       "      <td id=\"T_3c6b1_row145_col2\" class=\"data row145 col2\" >Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....</td>\n",
       "      <td id=\"T_3c6b1_row145_col3\" class=\"data row145 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row145_col4\" class=\"data row145 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row146_col0\" class=\"data row146 col0\" >validmind.data_validation.nlp.Toxicity</td>\n",
       "      <td id=\"T_3c6b1_row146_col1\" class=\"data row146 col1\" >Toxicity</td>\n",
       "      <td id=\"T_3c6b1_row146_col2\" class=\"data row146 col2\" >Analyzes the toxicity of text data within a dataset using a pre-trained toxicity model....</td>\n",
       "      <td id=\"T_3c6b1_row146_col3\" class=\"data row146 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row146_col4\" class=\"data row146 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row147_col0\" class=\"data row147 col0\" >validmind.data_validation.nlp.PolarityAndSubjectivity</td>\n",
       "      <td id=\"T_3c6b1_row147_col1\" class=\"data row147 col1\" >Polarity And Subjectivity</td>\n",
       "      <td id=\"T_3c6b1_row147_col2\" class=\"data row147 col2\" >Analyzes the polarity and subjectivity of text data within a dataset....</td>\n",
       "      <td id=\"T_3c6b1_row147_col3\" class=\"data row147 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row147_col4\" class=\"data row147 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row148_col0\" class=\"data row148 col0\" >validmind.data_validation.nlp.Punctuations</td>\n",
       "      <td id=\"T_3c6b1_row148_col1\" class=\"data row148 col1\" >Punctuations</td>\n",
       "      <td id=\"T_3c6b1_row148_col2\" class=\"data row148 col2\" >Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....</td>\n",
       "      <td id=\"T_3c6b1_row148_col3\" class=\"data row148 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row148_col4\" class=\"data row148 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row149_col0\" class=\"data row149 col0\" >validmind.data_validation.nlp.Sentiment</td>\n",
       "      <td id=\"T_3c6b1_row149_col1\" class=\"data row149 col1\" >Sentiment</td>\n",
       "      <td id=\"T_3c6b1_row149_col2\" class=\"data row149 col2\" >Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....</td>\n",
       "      <td id=\"T_3c6b1_row149_col3\" class=\"data row149 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row149_col4\" class=\"data row149 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row150_col0\" class=\"data row150 col0\" >validmind.data_validation.nlp.CommonWords</td>\n",
       "      <td id=\"T_3c6b1_row150_col1\" class=\"data row150 col1\" >Common Words</td>\n",
       "      <td id=\"T_3c6b1_row150_col2\" class=\"data row150 col2\" >Identifies and visualizes the 40 most frequent non-stopwords in a specified text column within a dataset....</td>\n",
       "      <td id=\"T_3c6b1_row150_col3\" class=\"data row150 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row150_col4\" class=\"data row150 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row151_col0\" class=\"data row151 col0\" >validmind.data_validation.nlp.Hashtags</td>\n",
       "      <td id=\"T_3c6b1_row151_col1\" class=\"data row151 col1\" >Hashtags</td>\n",
       "      <td id=\"T_3c6b1_row151_col2\" class=\"data row151 col2\" >Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....</td>\n",
       "      <td id=\"T_3c6b1_row151_col3\" class=\"data row151 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row151_col4\" class=\"data row151 col4\" >{'top_hashtags': 25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row152_col0\" class=\"data row152 col0\" >validmind.data_validation.nlp.LanguageDetection</td>\n",
       "      <td id=\"T_3c6b1_row152_col1\" class=\"data row152 col1\" >Language Detection</td>\n",
       "      <td id=\"T_3c6b1_row152_col2\" class=\"data row152 col2\" >Detects the language of each text entry in a dataset and visualizes the distribution of languages...</td>\n",
       "      <td id=\"T_3c6b1_row152_col3\" class=\"data row152 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row152_col4\" class=\"data row152 col4\" >{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row153_col0\" class=\"data row153 col0\" >validmind.data_validation.nlp.Mentions</td>\n",
       "      <td id=\"T_3c6b1_row153_col1\" class=\"data row153 col1\" >Mentions</td>\n",
       "      <td id=\"T_3c6b1_row153_col2\" class=\"data row153 col2\" >Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....</td>\n",
       "      <td id=\"T_3c6b1_row153_col3\" class=\"data row153 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row153_col4\" class=\"data row153 col4\" >{'top_mentions': 25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row154_col0\" class=\"data row154 col0\" >validmind.data_validation.nlp.TextDescription</td>\n",
       "      <td id=\"T_3c6b1_row154_col1\" class=\"data row154 col1\" >Text Description</td>\n",
       "      <td id=\"T_3c6b1_row154_col2\" class=\"data row154 col2\" >Performs comprehensive textual analysis on a dataset using NLTK, evaluating various parameters and generating...</td>\n",
       "      <td id=\"T_3c6b1_row154_col3\" class=\"data row154 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row154_col4\" class=\"data row154 col4\" >{'unwanted_tokens': {'dollar', ' ', 'ms', '``', 'mrs', 'mr', \"s'\", 'dr', \"'s\", \"''\", 'us', 's'}, 'num_top_words': 3, 'lang': 'english'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3c6b1_row155_col0\" class=\"data row155 col0\" >validmind.data_validation.nlp.StopWords</td>\n",
       "      <td id=\"T_3c6b1_row155_col1\" class=\"data row155 col1\" >Stop Words</td>\n",
       "      <td id=\"T_3c6b1_row155_col2\" class=\"data row155 col2\" >Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....</td>\n",
       "      <td id=\"T_3c6b1_row155_col3\" class=\"data row155 col3\" >['dataset']</td>\n",
       "      <td id=\"T_3c6b1_row155_col4\" class=\"data row155 col4\" >{'min_percent_threshold': 0.5, 'num_words': 25}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x37bf68c10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vm.tests.list_tests()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc6_'></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.\n",
    "\n",
    "<a id='toc6_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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "validmind-1QuffXMV-py3.9",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
