sklearn.ensemble
.RandomForestRegressor¶
- 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 underlyingDecisionTreeRegressor
. The sub-sample size is controlled with themax_samples
parameter ifbootstrap=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_estimatorsint, 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{“squared_error”, “absolute_error”, “friedman_mse”, “poisson”}, 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”.
New in version 0.18: Mean Absolute Error (MAE) criterion.
New in version 1.0: Poisson criterion.
- max_depthint, 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_splitint or float, default=2
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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
- min_samples_leafint or float, default=1
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 andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
- min_weight_fraction_leaffloat, default=0.0
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_features{“sqrt”, “log2”, None}, int or float, default=1.0
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 andmax(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.
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_nodesint, 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_decreasefloat, 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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
- bootstrapbool, default=True
Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
- oob_scorebool 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 signaturemetric(y_true, y_pred)
to use a custom metric. Only available ifbootstrap=True
.- n_jobsint, default=None
The number of jobs to run in parallel.
fit
,predict
,decision_path
andapply
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- random_stateint, 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 (ifmax_features < n_features
). See Glossary for details.- verboseint, default=0
Controls the verbosity when fitting and predicting.
- warm_startbool, 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 weak-learners for details.- ccp_alphanon-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.New in version 0.22.
- max_samplesint or float, default=None
If bootstrap is True, the number of samples to draw from X to train each base estimator.
If None (default), then draw
X.shape[0]
samples.If int, then draw
max_samples
samples.If float, then draw
max(round(n_samples * max_samples), 1)
samples. Thus,max_samples
should be in the interval(0.0, 1.0]
.
New in version 0.22.
- monotonic_cstarray-like of int of shape (n_features), default=None
- Indicates the monotonicity constraint to enforce on each feature.
1: monotonically increasing
0: no constraint
-1: monotonically decreasing
If monotonic_cst is None, no constraints are applied.
- Monotonicity constraints are not supported for:
multioutput regressions (i.e. when
n_outputs_ > 1
),regressions trained on data with missing values.
Read more in the User Guide.
New in version 1.4.
- Attributes:
- estimator_
DecisionTreeRegressor
The child estimator template used to create the collection of fitted sub-estimators.
New in version 1.2:
base_estimator_
was renamed toestimator_
.- estimators_list of DecisionTreeRegressor
The 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.
New 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.New in version 1.0.
- n_outputs_int
The 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.estimators_samples_
list of arraysThe subset of drawn samples for each base estimator.
- estimator_
See also
sklearn.tree.DecisionTreeRegressor
A decision tree regressor.
sklearn.ensemble.ExtraTreesRegressor
Ensemble of extremely randomized tree regressors.
sklearn.ensemble.HistGradientBoostingRegressor
A Histogram-based Gradient Boosting Regression Tree, very fast for big datasets (n_samples >= 10_000).
Notes
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
andbootstrap=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
usesn_features
rather thann_features / 3
. The latter was originally suggested in [1], whereas the former was more recently justified empirically in [2].References
[1]Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
[2]P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
Examples
>>> 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]
Methods
apply
(X)Apply trees in the forest to X, return leaf indices.
Return the decision path in the forest.
fit
(X, y[, sample_weight])Build a forest of trees from the training set (X, y).
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict regression target for X.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.- apply(X)[source]¶
Apply trees in the forest to X, return leaf indices.
- Parameters:
- X{array-like, sparse matrix} 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 sparsecsr_matrix
.
- Returns:
- X_leavesndarray 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.
- decision_path(X)[source]¶
Return the decision path in the forest.
New in version 0.18.
- Parameters:
- X{array-like, sparse matrix} 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 sparsecsr_matrix
.
- Returns:
- indicatorsparse 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_ptrndarray 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.
- property estimators_samples_¶
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.
- property 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
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.
- fit(X, y, sample_weight=None)[source]¶
Build a forest of trees from the training set (X, y).
- Parameters:
- X{array-like, sparse matrix} 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 sparsecsc_matrix
.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
- sample_weightarray-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:
- selfobject
Fitted estimator.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- predict(X)[source]¶
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{array-like, sparse matrix} 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 sparsecsr_matrix
.
- Returns:
- yndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted values.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-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)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
w.r.t.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') RandomForestRegressor [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_params(**params)[source]¶
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<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') RandomForestRegressor [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
Examples using sklearn.ensemble.RandomForestRegressor
¶
Release Highlights for scikit-learn 1.4
Release Highlights for scikit-learn 0.24
Combine predictors using stacking
Comparing Random Forests and Histogram Gradient Boosting models
Comparing random forests and the multi-output meta estimator
Plot individual and voting regression predictions
Imputing missing values before building an estimator
Imputing missing values with variants of IterativeImputer