Start by recognizing the large gap between training and validation AUC as a classic sign of overfitting and potential data leakage, then systematically investigate each component of the pipeline. Prioritize time-based validation to simulate real-world deployment, and address class imbalance and overconfidence through proper resampling, calibration, and regularization.
Pro tip: Always compare random-split validation to time-based holdout; if the gap is large, it indicates temporal leakage or drift. Use time-series cross-validation and monitor feature distributions over time to catch drift early.
Check for leakage by ensuring no future information is used in training. Verify that random split doesn't mix time periods, and switch to time-based splits. Examine feature importance for suspiciously predictive features that may leak target information.
Compare distributions of features and labels between training and validation/holdout sets over time. Use statistical tests (e.g., KS test) and monitor for concept drift. Investigate if the churn definition or data collection process changed.
Implement time-series cross-validation (e.g., rolling window) to better estimate generalization. Perform error analysis on misclassified examples, especially false negatives, to identify patterns and potential feature gaps.
Perform feature ablations to identify which features contribute to overfitting. Apply regularization (L1/L2, dropout), reduce model complexity, and address class imbalance with techniques like SMOTE, class weights, or focal loss. Calibrate probabilities using Platt scaling or isotonic regression.
Use a robust evaluation framework with time-based holdout and business metrics (e.g., lift, ROI). Implement continuous monitoring for drift and automated retraining. Set up alerts for performance degradation and maintain a feedback loop.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.