class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)
[source]
A random forest regressor.
A random forest is a meta estimator that fits a number of classifying decision trees on various subsamples of the dataset and use averaging to improve the predictive accuracy and control overfitting. The subsample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True
(default).
Read more in the User Guide.
Parameters: 
n_estimators : integer, optional (default=10) The number of trees in the forest. criterion : string, optional (default=”mse”) The function to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “mae” for the mean absolute error. New in version 0.18: Mean Absolute Error (MAE) criterion. max_features : int, float, string or None, optional (default=”auto”) The number of features to consider when looking for the best split:
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_depth : integer or None, optional (default=None) 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. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node:
Changed in version 0.18: Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node:
Changed in version 0.18: Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) 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. max_leaf_nodes : int or None, optional (default=None) Grow trees with min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. Deprecated since version 0.19: min_impurity_decrease : float, optional (default=0.) 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
New in version 0.19. bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. oob_score : bool, optional (default=False) whether to use outofbag samples to estimate the R^2 on unseen data. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to 

Attributes: 
estimators_ : list of DecisionTreeRegressor The collection of fitted subestimators. feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). n_features_ : int The number of features when n_outputs_ : int The number of outputs when oob_score_ : float Score of the training dataset obtained using an outofbag estimate. oob_prediction_ : array of shape = [n_samples] Prediction computed with outofbag estimate on the training set. 
See also
DecisionTreeRegressor
, ExtraTreesRegressor
The default values for the parameters controlling the size of the trees (e.g. max_depth
, min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features
and bootstrap=False
, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state
has to be fixed.
[R167] 

>>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.datasets import make_regression >>> >>> X, y = make_regression(n_features=4, n_informative=2, ... random_state=0, shuffle=False) >>> regr = RandomForestRegressor(max_depth=2, random_state=0) >>> regr.fit(X, y) RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=2, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=0, verbose=0, warm_start=False) >>> print(regr.feature_importances_) [ 0.17339552 0.81594114 0. 0.01066333] >>> print(regr.predict([[0, 0, 0, 0]])) [2.50699856]
apply (X)  Apply trees in the forest to X, return leaf indices. 
decision_path (X)  Return the decision path in the forest 
fit (X, y[, sample_weight])  Build a forest of trees from the training set (X, y). 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict regression target for X. 
score (X, y[, sample_weight])  Returns the coefficient of determination R^2 of the prediction. 
set_params (**params)  Set the parameters of this estimator. 
__init__(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)
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apply(X)
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Apply trees in the forest to X, return leaf indices.
Parameters: 
X : arraylike or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to 

Returns: 
X_leaves : array_like, shape = [n_samples, n_estimators] For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in. 
decision_path(X)
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Return the decision path in the forest
New in version 0.18.
Parameters: 
X : arraylike or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to 

Returns: 
indicator : sparse csr array, shape = [n_samples, n_nodes] Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. n_nodes_ptr : array of size (n_estimators + 1, ) The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator. 
feature_importances_
Returns:  feature_importances_ : array, shape = [n_features] 

fit(X, y, sample_weight=None)
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Build a forest of trees from the training set (X, y).
Parameters: 
X : arraylike or sparse matrix of shape = [n_samples, n_features] The training input samples. Internally, its dtype will be converted to y : arraylike, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels in classification, real numbers in regression). sample_weight : arraylike, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. 

Returns: 
self : object Returns self. 
get_params(deep=True)
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Get parameters for this estimator.
Parameters: 
deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. 

Returns: 
params : mapping of string to any Parameter names mapped to their values. 
predict(X)
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Predict regression target for X.
The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.
Parameters: 
X : arraylike or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to 

Returns: 
y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted values. 
score(X, y, sample_weight=None)
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Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: 
X : arraylike, shape = (n_samples, n_features) Test samples. y : arraylike, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : arraylike, shape = [n_samples], optional Sample weights. 

Returns: 
score : float R^2 of self.predict(X) wrt. y. 
set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns:  self : 

sklearn.ensemble.RandomForestRegressor
© 2007–2017 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html