Hyperparameter Tuning

35 min
Block 1 — Optimization as search
Objective
formalize hyperparameter tuning as an outer optimization loop over a validation objective with a noisy, expensive, non-differentiable, mixed-type search space. Master GridSearchCV and RandomizedSearchCV in their full semantics. Measure the empirical superiority of random search on sparse problems. Understand successive halving as a budget-aware strategy. Quantify the winner's curse, implement nested cross-validation for honest assessment, and select a search strategy by budget and problem structure.
Estimated duration
90 minutes
Prerequisites
chapters 009, 030, 034
Associated quizzes
035.1-quiz-gridsearch.md to 035.8-quiz-strategy-selection.md

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This lesson is part of the “Cross-Validation and Hyperparameter Tuning” module

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