Model Evaluation and Feature Selection

By the end, you'll be able to:

  • Design reliable train/validation/test workflows.
  • Choose metrics that match regression, classification, and ranking tasks.
  • Use cross-validation, feature selection, and tuning without leaking information.
  • Bias-variance trade-off and learning curves
  • Regression, classification, and ranking metrics
  • k-fold, stratified, repeated, and time-series cross-validation
  • Filter, wrapper, and embedded feature selection
  • Hyperparameter tuning, early stopping, and pruning
  • Imbalanced data, resampling, and threshold selection
Foundations of Model Evaluation