Hyperparameter Tuning Implementation

HardHyperparameter TuningModel SelectionCross-ValidationGrid SearchRandom SearchOptimization

Implement grid search and random search for hyperparameter tuning from scratch. Compare different search strategies and understand their impact on model performance.

Problem:

Implement grid search and random search for hyperparameter tuning from scratch. Compare different search strategies and understand their impact on model performance.

Examples:

Input: param_grid = {"C": [0.1, 1], "kernel": ["linear", "rbf"]}
grid_search_cv(SVC(), param_grid, X, y)
Output: Best parameters: {'C': 1, 'kernel': 'rbf'}
Best CV score: 0.9500
Grid search example with SVM parameters
Input: param_dist = {"C": np.logspace(-1, 1, 100)}
random_search_cv(SVC(), param_dist, X, y, n_iter=5)
Output: Best parameters found after 5 iterations
Best CV score: 0.9450
Random search example with continuous parameter

Constraints:

  • Must implement both grid and random search
  • Must use cross-validation for evaluation
  • Must handle multiple parameter types

Code Editorpython

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Output

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