class sklearn.ensemble. 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 , monotonic_cst = None ) [source] #
A random forest regressor.
A random forest is a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. Trees in the forest use the best split strategy, i.e. equivalent to passing splitter="best" to the underlying DecisionTreeRegressor . The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.
For a comparison between tree-based ensemble models see the example Comparing Random Forests and Histogram Gradient Boosting models .
Read more in the User Guide .
Parameters : n_estimators int, default=100
The number of trees in the forest.
Changed in version 0.22: The default value of n_estimators changed from 10 to 100 in 0.22.
criterion , default=”squared_error”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”.
Added in version 0.18: Mean Absolute Error (MAE) criterion.
Added in version 1.0: Poisson criterion.
max_depth int, default=NoneThe 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 or float, default=2
The minimum number of samples required to split an internal node:
Changed in version 0.18: Added float values for fractions.
min_samples_leaf int or float, default=1The 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.
Changed in version 0.18: Added float values for fractions.
min_weight_fraction_leaf float, default=0.0The 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_features , int or float, default=1.0
The number of features to consider when looking for the best split:
The default of 1.0 is equivalent to bagged trees and more randomness can be achieved by setting smaller values, e.g. 0.3.
Changed in version 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.
max_leaf_nodes int, default=None
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.
min_impurity_decrease float, default=0.0
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 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.
Added in version 0.19.
bootstrap bool, default=TrueWhether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_score bool or callable, default=False
Whether to use out-of-bag samples to estimate the generalization score. By default, r2_score is used. Provide a callable with signature metric(y_true, y_pred) to use a custom metric. Only available if bootstrap=True .
n_jobs int, default=None
The number of jobs to run in parallel. fit , predict , decision_path and apply are all parallelized over the trees. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
random_state int, RandomState instance or None, default=None
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 Glossary for details.
verbose int, default=0
Controls the verbosity when fitting and predicting.
warm_start bool, default=False
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 Glossary and Fitting additional trees for details.
ccp_alpha non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See Minimal Cost-Complexity Pruning for details.
Added in version 0.22.
max_samples int or float, default=NoneIf bootstrap is True, the number of samples to draw from X to train each base estimator.
Added in version 0.22.
If monotonic_cst is None, no constraints are applied.
Read more in the User Guide .
Added in version 1.4.
Attributes : estimator_ DecisionTreeRegressorThe child estimator template used to create the collection of fitted sub-estimators.
Added in version 1.2: base_estimator_ was renamed to estimator_ .
estimators_ list of DecisionTreeRegressorThe collection of fitted sub-estimators.
feature_importances_ ndarray of shape (n_features,)
The impurity-based feature importances.
n_features_in_ int
Number of features seen during fit .
Added in version 0.24.
feature_names_in_ ndarray of shape ( n_features_in_ ,)Names of features seen during fit . Defined only when X has feature names that are all strings.
Added in version 1.0.
n_outputs_ intThe number of outputs when fit is performed.
oob_score_ float
Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.
oob_prediction_ ndarray of shape (n_samples,) or (n_samples, n_outputs)
Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score is True.
The subset of drawn samples for each base estimator.
A decision tree regressor.
Ensemble of extremely randomized tree regressors.
A Histogram-based Gradient Boosting Regression Tree, very fast for big datasets (n_samples >= 10_000).
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.
The default value max_features=1.0 uses n_features rather than n_features / 3 . The latter was originally suggested in [1], whereas the former was more recently justified empirically in [2].
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
>>> 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(. ) >>> print(regr.predict([[0, 0, 0, 0]])) [-8.32987858]
Apply trees in the forest to X, return leaf indices.
Parameters : X of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32 . If a sparse matrix is provided, it will be converted into a sparse csr_matrix .
Returns : X_leaves ndarray of 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.
Return the decision path in the forest.
Added in version 0.18.
Parameters : X of shape (n_samples, n_features)The input samples. Internally, its dtype will be converted to dtype=np.float32 . If a sparse matrix is provided, it will be converted into a sparse csr_matrix .
Returns : indicator sparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
n_nodes_ptr ndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.
The subset of drawn samples for each base estimator.
Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.
Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.
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 sklearn.inspection.permutation_importance as an alternative.
Returns : feature_importances_ ndarray of shape (n_features,)
The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.
Build a forest of trees from the training set (X, y).
Parameters : X of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted to dtype=np.float32 . If a sparse matrix is provided, it will be converted into a sparse csc_matrix .
y array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
sample_weight array-like of shape (n_samples,), default=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
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns : routing MetadataRequest
A MetadataRequest encapsulating routing information.
Get parameters for this estimator.
Parameters : deep bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns : params dict
Parameter names mapped to their values.
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 of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32 . If a sparse matrix is provided, it will be converted into a sparse csr_matrix .
Returns : y ndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted values.
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac)\) , 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 array-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted) , where n_samples_fitted is the number of samples used in the fitting for the estimator.
y array-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X .
sample_weight array-like of shape (n_samples,), default=None
Returns : score float
\(R^2\) of self.predict(X) w.r.t. y .
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score . This influences the score method of all the multioutput regressors (except for MultiOutputRegressor ).
Request metadata passed to the fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config ). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
The default ( sklearn.utils.metadata_routing.UNCHANGED ) retains the existing request. This allows you to change the request for some parameters and not others.
Added in version 1.3.
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline . Otherwise it has no effect.
Parameters : sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit .
Returns : self object
The updated object.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline ). The latter have parameters of the form __ so that it’s possible to update each component of a nested object.
Parameters : **params dict
Returns : self estimator instance
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config ). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
The default ( sklearn.utils.metadata_routing.UNCHANGED ) retains the existing request. This allows you to change the request for some parameters and not others.
Added in version 1.3.
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline . Otherwise it has no effect.
Parameters : sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score .
Returns : self object