Classical ML

Model Evaluation

How to measure a model honestly — validation protocols, metrics, and what to do when classes are rare

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Class imbalance
core
When positives are rare, default thresholds and ROC curves flatter the model — threshold tuning, class weights, resampling, and PR-AUC are the tools, and cost-sensitive framing is the principle that unifies them
10 minmodel-evaluationimbalance
Classification metrics
core
Precision, recall, F1, and ROC-AUC from one confusion matrix — what each number actually measures, why accuracy misleads, and how to read AUC as a ranking probability
11 minmodel-evaluationmetrics
Cross-validation
core
k-fold, stratified, leave-one-out, time-series splits, and nested CV — which protocol estimates generalization honestly, and the leakage traps that make CV numbers lie
9 minmodel-evaluationvalidation