Model Evaluation
Accuracy, precision, recall, F1, ROC curves, and cross-validation for reliable evaluation.
A model is only useful if we can measure how well it generalises. This chapter covers classification metrics — accuracy, precision, recall, F1, ROC-AUC — regression metrics, k-fold cross-validation, and the common mistakes that lead to over-optimistic evaluation.