Model evaluation and validation: Data Science course | Zoonk
42. Model evaluation and validation
Covers train-test splits, cross-validation, leakage, confusion matrices, ROC curves, precision, recall, and model selection. Learners judge models by the decision they must support, not just by a single score.
