ClassifierPerformance

Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy, and ROC AUC scores.

Purpose

The Classifier Performance test is designed to evaluate the performance of Machine Learning classification models. It accomplishes this by computing precision, recall, F1-Score, and accuracy, as well as the ROC AUC (Receiver operating characteristic - Area under the curve) scores, thereby providing a comprehensive analytic view of the models’ performance. The test is adaptable, handling binary and multiclass models equally effectively.

Test Mechanism

The test produces a report that includes precision, recall, F1-Score, and accuracy, by leveraging the classification_report from scikit-learn’s metrics module. For multiclass models, macro and weighted averages for these scores are also calculated. Additionally, the ROC AUC scores are calculated and included in the report using the multiclass_roc_auc_score function. The outcome of the test (report format) differs based on whether the model is binary or multiclass.

Signs of High Risk

• Low values for precision, recall, F1-Score, accuracy, and ROC AUC, indicating poor performance.
• Imbalance in precision and recall scores.
• A low ROC AUC score, especially scores close to 0.5 or lower, suggesting a failing model.

Strengths

• Versatile, capable of assessing both binary and multiclass models.
• Utilizes a variety of commonly employed performance metrics, offering a comprehensive view of model performance.
• The use of ROC-AUC as a metric is beneficial for evaluating unbalanced datasets.

Limitations

• Assumes correctly identified labels for binary classification models.
• Specifically designed for classification models and not suitable for regression models.
• May provide limited insights if the test dataset does not represent real-world scenarios adequately.