Provide a callable with signature metric(y_true, y_pred) to use aĬustom metric.

Whether to use out-of-bag samples to estimate the generalization score. oob_score bool or callable, default=False Whole dataset is used to build each tree. Whether bootstrap samples are used when building trees.

Parameters : n_estimators int, default=100 The sub-sample size is controlled with the max_samples parameter ifīootstrap=True (default), otherwise the whole dataset is used to buildįor a comparison between tree-based ensemble models see the exampleĬomparing Random Forests and Histogram Gradient Boosting models. To improve the predictive accuracy and control over-fitting. RandomForestRegressor ( n_estimators = 100, *, criterion = 'squared_error', max_depth = None, min_samples_split = 2, min_samples_leaf = 1, min_weight_fraction_leaf = 0.0, max_features = 1.0, max_leaf_nodes = None, min_impurity_decrease = 0.0, bootstrap = True, oob_score = False, n_jobs = None, random_state = None, verbose = 0, warm_start = False, ccp_alpha = 0.0, max_samples = None ) ¶Ī random forest is a meta estimator that fits a number of classifyingĭecision trees on various sub-samples of the dataset and uses averaging

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