Hyperparameter Tuning and Model Selection
Apply systematic approaches to find optimal hyperparameters and select between competing models.
Machine learning models have hyperparameters—settings fixed before training that control learning behavior (e.g., regularization strength, tree depth, learning rate). Unlike model parameters learned from data, hyperparameters must be chosen by practitioners. Grid search exhaustively evaluates all combinations of candidate values; random search samples randomly, often more efficient in high-dimensional spaces. Bayesian optimization uses prior models to intelligently select promising combinations. Hyperparameter tuning coupled with cross-validation prevents overfitting to a particular train-test split. After tuning, comparing candidates requires statistical tests to verify improvements aren't due to chance. This process unites machine learning (which emphasizes empirical model selection) and statistics (which emphasizes…
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