OverfitDiagnosis

Assesses potential overfitting in a model’s predictions, identifying regions where performance between training and testing sets deviates significantly.

Purpose

The Overfit Diagnosis test aims to identify areas in a model’s predictions where there is a significant difference in performance between the training and testing sets. This test helps to pinpoint specific regions or feature segments where the model may be overfitting.

Test Mechanism

This test compares the model’s performance on training versus test data, grouped by feature columns. It calculates the difference between the training and test performance for each group and identifies regions where this difference exceeds a specified threshold:

• The test works for both classification and regression models.
• It defaults to using the AUC metric for classification models and the MSE metric for regression models.
• The threshold for identifying overfitting regions is set to 0.04 by default.
• The test calculates the performance metrics for each feature segment and plots regions where the performance gap exceeds the threshold.

Signs of High Risk

• Significant gaps between training and test performance metrics for specific feature segments.
• Multiple regions with performance gaps exceeding the defined threshold.
• Higher than expected differences in predicted versus actual values in the test set compared to the training set.

Strengths

• Identifies specific areas where overfitting occurs.
• Supports multiple performance metrics, providing flexibility.
• Applicable to both classification and regression models.
• Visualization of overfitting segments aids in better understanding and debugging.

Limitations

• The default threshold may not be suitable for all use cases and requires tuning.
• May not capture more subtle forms of overfitting that do not exceed the threshold.
• Assumes that the binning of features adequately represents the data segments.