97f4cedd0caf8b70b0688c939aac825662a99b8b,examples/plot_random_vs_gp.py,,compute_mean_validation_score,#Any#,19

Before Change

    params = {
        "max_depth": [max_depth], "max_features": [max_features],
        "min_samples_split": [mss], "min_samples_leaf": [msl]}
    gscv = GridSearchCV(rfc, params, n_jobs=-1)
    gscv.fit(X, y)
    return -gscv.best_score_

// Bounds inspired by
// http://scikit-learn.org/dev/auto_examples/model_selection/randomized_search.html//example-model-selection-randomized-search-py
bounds = [(3, 50), (1, 12), (1, 12), (1, 12)]

After Change

    forest_params = [int(param) for param in forest_params]
    max_depth, max_features, mss, msl = forest_params

    rfc.set_params(
        max_depth=max_depth, max_features=max_features,
        min_samples_split=mss, min_samples_leaf=msl)
    return -np.mean(cross_val_score(rfc, X, y, cv=3, n_jobs=-1))

// Bounds inspired by
// http://scikit-learn.org/dev/auto_examples/model_selection/randomized_search.html//example-model-selection-randomized-search-py

Instances