Model Evaluation & Experimentation
Validation, metrics, and A/B testing for ML systems.
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Validation
Train/test splits, cross-validation, time-series CV, and data leakage.
Metrics (Classification)
Accuracy, precision/recall, F1, ROC/AUC, PR curves, and calibration.
Metrics (Regression)
RMSE, MAE, R-squared, and when R-squared is misleading.
Experimentation
A/B testing, randomization, confounders, Simpson's paradox, offline vs online evaluation.