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On this page

  • ClassifierPerformance
    • Purpose
    • Test Mechanism
    • Signs of High Risk
    • Strengths
    • Limitations
  • Edit this page
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  1. Test descriptions
  2. Model Validation
  3. Sklearn
  4. ClassifierPerformance

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.
CalibrationCurve
ClassifierThresholdOptimization

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