.. _`Random Forest Regressor`: .. _`com.sympathyfordata.advancedmachinelearning.randomforestregressor`: Random Forest Regressor ``````````````````````` .. image:: forest.svg :width: 48 A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the data set and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default). Documentation ::::::::::::: Attributes ========== **feature_importances_** The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. **n_features_** **n_outputs_** The number of outputs when ``fit`` is performed. **oob_prediction_** Prediction computed with out-of-bag estimate on the training set. This attribute exists only when ``oob_score`` is True. **oob_score_** Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True. Definition :::::::::: Output ports ============ **model** model Model Configuration ============= **Bootstrap** (bootstrap) Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. **Split quality criterion** (criterion) The function to measure the quality of a split. Supported criteria are "squared_error" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, "friedman_mse", which uses mean squared error with Friedman's improvement score for potential splits, "absolute_error" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and "poisson" which uses reduction in Poisson deviance to find splits. Training using "absolute_error" is significantly slower than when using "squared_error". .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. .. versionadded:: 1.0 Poisson criterion. **Maximum tree depth** (max_depth) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. **Maximum number of features** (max_features) The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `max(1, int(max_features * n_features_in_))` features are considered at each split. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None or 1.0, then `max_features=n_features`. .. note:: The default of 1.0 is equivalent to bagged trees and more randomness can be achieved by setting smaller values, e.g. 0.3. .. versionchanged:: 1.1 The default of `max_features` changed from `"auto"` to 1.0. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. **Maximum leaf nodes** (max_leaf_nodes) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. **Minimum impurity decrease** (min_impurity_decrease) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 **Growth threshold** (min_impurity_split) (no description) **Minimum number of samples for leaf node** (min_samples_leaf) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. **Minimum samples for split** (min_samples_split) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for fractions. **Minimum leaf weight fraction** (min_weight_fraction_leaf) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. **Trees in forest** (n_estimators) The number of trees in the forest. .. versionchanged:: 0.22 The default value of ``n_estimators`` changed from 10 to 100 in 0.22. **Number of jobs** (n_jobs) The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. **Use out-of-bag samples** (oob_score) Whether to use out-of-bag samples to estimate the generalization score. By default, :func:`~sklearn.metrics.r2_score` is used. Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`. **Random Seed** (random_state) Controls both the randomness of the bootstrapping of the samples used when building trees (if ``bootstrap=True``) and the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``). See random_state for details. **Warm start** (warm_start) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See warm_start and tree_ensemble_warm_start for details. Examples ======== * :download:`random_forest_regressor.syx ` Implementation ============== .. automodule:: node_RandomForestRegressor :noindex: .. class:: RandomForestRegressor :noindex: