Introduction
Learn what hyperparameter tuning estimates, how to design valid search spaces, and why final evaluation needs an outer boundary.
Cross-Validation Techniques
Choose IID, stratified, grouped, temporal, and nested cross-validation designs that match deployment.
Grid Search vs. Random Search
Compare grid and random search with equal candidate budgets, fold-local preprocessing, and an isolated test set.
Tuning by Algorithm
Build model-specific, conditional search spaces for trees, SVMs, KNN, and gradient boosting.
Bayesian Optimization
Use sequential model-based hyperparameter optimization with reproducible spaces, cross-validated objectives, and isolated evaluation.
Best Practices
Design reproducible, resource-aware hyperparameter searches without leakage or optimistic evaluation.