An extra-trees regressor.
This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
This estimator has native support for missing values (NaNs) for random splits. During training, a random threshold will be chosen to split the non-missing values on. Then the non-missing values will be sent to the left and right child based on the randomly selected threshold, while the missing values will also be randomly sent to the left or right child. This is repeated for every feature considered at each split. The best split among these is chosen.
Read more in the User Guide.
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.
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.
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.
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.
Changed in version 0.18: Added float values for fractions.
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.
Changed in version 0.18: Added float values for fractions.
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.
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.
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.
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.
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.
Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
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
.
For an illustration of out-of-bag (OOB) error estimation, see the example OOB Errors for Random Forests.
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.
Controls 3 sources of randomness:
the bootstrapping of the samples used when building trees (if bootstrap=True
)
the sampling of the features to consider when looking for the best split at each node (if max_features < n_features
)
the draw of the splits for each of the max_features
See Glossary for details.
Controls the verbosity when fitting and predicting.
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.
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. See Post pruning decision trees with cost complexity pruning for an example of such pruning.
Added in version 0.22.
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_samples * X.shape[0]
samples. Thus, max_samples
should be in the interval (0.0, 1.0]
.
Added in version 0.22.
1: monotonically increasing
0: no constraint
-1: monotonically decreasing
If monotonic_cst is None, no constraints are applied.
multioutput regressions (i.e. when n_outputs_ > 1
),
regressions trained on data with missing values.
Read more in the User Guide.
Added in version 1.4.
ExtraTreeRegressor
The child estimator template used to create the collection of fitted sub-estimators.
Added in version 1.2: base_estimator_
was renamed to estimator_
.
The collection of fitted sub-estimators.
feature_importances_
ndarray of shape (n_features,)
The impurity-based feature importances.
Number of features seen during fit.
Added in version 0.24.
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.
The number of outputs.
Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score
is True.
Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score
is True.
estimators_samples_
list of arrays
The subset of drawn samples for each base estimator.
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.
References
[1]P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
Examples
>>> from sklearn.datasets import load_diabetes >>> from sklearn.model_selection import train_test_split >>> from sklearn.ensemble import ExtraTreesRegressor >>> X, y = load_diabetes(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit( ... X_train, y_train) >>> reg.score(X_test, y_test) 0.2727...
Apply trees in the forest to X, return leaf indices.
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
.
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.
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
.
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.
Build a forest of trees from the training set (X, y).
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
.
The target values (class labels in classification, real numbers in regression).
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.
Fitted estimator.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
A MetadataRequest
encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
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.
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
.
The predicted values.
Return coefficient of determination on test data.
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 of y
, disregarding the input features, would get a \(R^2\) score of 0.0.
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.
True values for X
.
Sample weights.
\(R^2\) of self.predict(X)
w.r.t. y
.
Notes
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
).
Configure whether metadata should be requested to be passed to the fit
method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the 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.Added in version 1.3.
Metadata routing for sample_weight
parameter in fit
.
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 <component>__<parameter>
so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Configure whether metadata should be requested to be passed to the score
method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the 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.Added in version 1.3.
Metadata routing for sample_weight
parameter in score
.
The updated object.
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