ConfusionMatrix

Evaluates and visually represents the classification ML model’s predictive performance using a Confusion Matrix heatmap.

Purpose: The Confusion Matrix tester is designed to assess the performance of a classification Machine Learning model. This performance is evaluated based on how well the model is able to correctly classify True Positives, True Negatives, False Positives, and False Negatives - fundamental aspects of model accuracy.

Test Mechanism: The mechanism used involves taking the predicted results (y_test_predict) from the classification model and comparing them against the actual values (y_test_true). A confusion matrix is built using the unique labels extracted from y_test_true, employing scikit-learn’s metrics. The matrix is then visually rendered with the help of Plotly’s create_annotated_heatmap function. A heatmap is created which provides a two-dimensional graphical representation of the model’s performance, showcasing distributions of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).

Signs of High Risk: Indicators of high risk related to the model include: - High numbers of False Positives (FP) and False Negatives (FN), depicting that the model is not effectively classifying the values. - Low numbers of True Positives (TP) and True Negatives (TN), implying that the model is struggling with correctly identifying class labels.

Strengths: The Confusion Matrix tester brings numerous strengths: - It provides a simplified yet comprehensive visual snapshot of the classification model’s predictive performance. - It distinctly brings out True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), thus, making it easier to focus on potential areas of improvement. - The matrix is beneficial in dealing with multi-class classification problems as it can provide a simple view of complex model performances. - It aids in understanding the different types of errors that the model could potentially make, as it provides in-depth insights into Type-I and Type-II errors.

Limitations: Despite its various strengths, the Confusion Matrix tester does exhibit some limitations: - In cases of unbalanced classes, the effectiveness of the confusion matrix might be lessened. It may wrongly interpret the accuracy of a model that is essentially just predicting the majority class. - It does not provide a single unified statistic that could evaluate the overall performance of the model. Different aspects of the model’s performance are evaluated separately instead. - It mainly serves as a descriptive tool and does not offer the capability for statistical hypothesis testing. - Risks of misinterpretation exist because the matrix doesn’t directly provide precision, recall, or F1-score data. These metrics have to be computed separately.