Foundations of Model Building & Evaluation

Derive the bias-variance tradeoff decomposition for predictive models.

Compute and interpret precision, recall, F1-score, and ROC-AUC for classification with imbalanced data.

Evaluate regression models using RMSE, MAE, and R-squared, comparing their sensitivities to outliers.

Differentiate the use-cases for k-fold, stratified, and time-series cross-validation strategies.

Analyze the risks of data leakage when applying cross-validation and preprocessing steps.