class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]

A Bagging regressor. A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]. If samples are drawn with replacement, then the method is known as Bagging [2]. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]. Read more in the User Guide.  New in version 0.15.   Parameters

base_estimatorobject, default=None

The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a DecisionTreeRegressor.
n_estimatorsint, default=10

The number of base estimators in the ensemble.
max_samplesint or float, default=1.0

The number of samples to draw from X to train each base estimator (with replacement by default, see bootstrap for more details).  If int, then draw max_samples samples. If float, then draw max_samples * X.shape[0] samples.
max_featuresint or float, default=1.0

The number of features to draw from X to train each base estimator ( without replacement by default, see bootstrap_features for more details).  If int, then draw max_features features. If float, then draw max_features * X.shape[1] features.
bootstrapbool, default=True

Whether samples are drawn with replacement. If False, sampling without replacement is performed.
bootstrap_featuresbool, default=False

Whether features are drawn with replacement.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization error.
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 ensemble. See the Glossary.
n_jobsint, default=None

The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
random_stateint, RandomState instance or None, default=None

Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See Glossary.
verboseint, default=0

Controls the verbosity when fitting and predicting.    Attributes

base_estimator_estimator

The base estimator from which the ensemble is grown.
n_features_int

The number of features when fit is performed.
estimators_list of estimators

The collection of fitted sub-estimators.
estimators_samples_list of arrays

The subset of drawn samples for each base estimator.
estimators_features_list of arrays

The subset of drawn features for each base estimator.
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,)

Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_prediction_ might contain NaN. This attribute exists only when oob_score is True.     References
1
L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.
2
L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.
3
T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.
4
G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.   Examples >>> from sklearn.svm import SVR
>>> from sklearn.ensemble import BaggingRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=4,
...                        n_informative=2, n_targets=1,
...                        random_state=0, shuffle=False)
>>> regr = BaggingRegressor(base_estimator=SVR(),
...                         n_estimators=10, random_state=0).fit(X, y)
>>> regr.predict([[0, 0, 0, 0]])
array([-2.8720...])
 Methods
fit(X, y[, sample_weight]) Build a Bagging ensemble of estimators from the training
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 \(R^2\) of the prediction.
set_params(**params) Set the parameters of this 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.

fit(X, y, sample_weight=None) [source]

 Build a Bagging ensemble of estimators from the training

set (X, y).    Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of shape (n_samples,)

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. Note that this is supported only if the base estimator supports sample weighting.    Returns

selfobject

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 estimators in the ensemble.  Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.    Returns

yndarray of shape (n_samples,)

The predicted values.

score(X, y, sample_weight=None) [source]

Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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.  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), where n_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) wrt. 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).

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.


sklearn.ensemble.BaggingRegressor
class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]

A Bagging regressor. A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]. If samples are drawn with replacement, then the method is known as Bagging [2]. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]. Read more in the User Guide.  New in version 0.15.   Parameters

base_estimatorobject, default=None

The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a DecisionTreeRegressor.
n_estimatorsint, default=10

The number of base estimators in the ensemble.
max_samplesint or float, default=1.0

The number of samples to draw from X to train each base estimator (with replacement by default, see bootstrap for more details).  If int, then draw max_samples samples. If float, then draw max_samples * X.shape[0] samples.
max_featuresint or float, default=1.0

The number of features to draw from X to train each base estimator ( without replacement by default, see bootstrap_features for more details).  If int, then draw max_features features. If float, then draw max_features * X.shape[1] features.
bootstrapbool, default=True

Whether samples are drawn with replacement. If False, sampling without replacement is performed.
bootstrap_featuresbool, default=False

Whether features are drawn with replacement.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization error.
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 ensemble. See the Glossary.
n_jobsint, default=None

The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
random_stateint, RandomState instance or None, default=None

Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See Glossary.
verboseint, default=0

Controls the verbosity when fitting and predicting.    Attributes

base_estimator_estimator

The base estimator from which the ensemble is grown.
n_features_int

The number of features when fit is performed.
estimators_list of estimators

The collection of fitted sub-estimators.
estimators_samples_list of arrays

The subset of drawn samples for each base estimator.
estimators_features_list of arrays

The subset of drawn features for each base estimator.
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,)

Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_prediction_ might contain NaN. This attribute exists only when oob_score is True.     References
1
L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.
2
L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.
3
T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.
4
G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.   Examples >>> from sklearn.svm import SVR
>>> from sklearn.ensemble import BaggingRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=4,
...                        n_informative=2, n_targets=1,
...                        random_state=0, shuffle=False)
>>> regr = BaggingRegressor(base_estimator=SVR(),
...                         n_estimators=10, random_state=0).fit(X, y)
>>> regr.predict([[0, 0, 0, 0]])
array([-2.8720...])
 Methods
fit(X, y[, sample_weight]) Build a Bagging ensemble of estimators from the training
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 \(R^2\) of the prediction.
set_params(**params) Set the parameters of this 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.

fit(X, y, sample_weight=None) [source]

 Build a Bagging ensemble of estimators from the training

set (X, y).    Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of shape (n_samples,)

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. Note that this is supported only if the base estimator supports sample weighting.    Returns

selfobject

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 estimators in the ensemble.  Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.    Returns

yndarray of shape (n_samples,)

The predicted values.

score(X, y, sample_weight=None) [source]

Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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.  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), where n_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) wrt. 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).

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.

 Examples using sklearn.ensemble.BaggingRegressor

  Single estimator versus bagging: bias-variance decomposition


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.


fit(X, y, sample_weight=None) [source]

 Build a Bagging ensemble of estimators from the training

set (X, y).    Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of shape (n_samples,)

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. Note that this is supported only if the base estimator supports sample weighting.    Returns

selfobject


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 estimators in the ensemble.  Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.    Returns

yndarray of shape (n_samples,)

The predicted values.


score(X, y, sample_weight=None) [source]

Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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.  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), where n_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) wrt. 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).


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.


class sklearn.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', 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=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None) [source]

An extra-trees classifier. 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. 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{“gini”, “entropy”}, default=”gini”

The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
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 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.
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 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.
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{“auto”, “sqrt”, “log2”}, int or float, default=”auto”

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 round(max_features * n_features) features are considered at each split. If “auto”, then max_features=sqrt(n_features). If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.  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, 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.  New in version 0.19.
min_impurity_splitfloat, default=None

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_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.
bootstrapbool, default=False

Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization accuracy.
n_jobsint, 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_stateint, RandomState instance or None, default=None

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.
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 the Glossary.
class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None

Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
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_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0, 1).   New in version 0.22.     Attributes

base_estimator_ExtraTreesClassifier

The child estimator template used to create the collection of fitted sub-estimators.
estimators_list of DecisionTreeClassifier

The collection of fitted sub-estimators.
classes_ndarray of shape (n_classes,) or a list of such arrays

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
n_classes_int or list

The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).
feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.
n_features_int

The number of features when fit is performed.
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_decision_function_ndarray of shape (n_samples, n_classes)

Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN. This attribute exists only when oob_score is True.      See also
sklearn.tree.ExtraTreeClassifier

Base classifier for this ensemble.
RandomForestClassifier

Ensemble Classifier based on trees with optimal splits.    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.ensemble import ExtraTreesClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0)
>>> clf.fit(X, y)
ExtraTreesClassifier(random_state=0)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
 Methods
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 class for X.
predict_log_proba(X) Predict class log-probabilities for X.
predict_proba(X) Predict class probabilities for X.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
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 sparse csr_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 sparse csr_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 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 sparse csc_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

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 class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.  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 sparse csr_matrix.    Returns

yndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes.

predict_log_proba(X) [source]

Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities 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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba(X) [source]

Predict class probabilities for X. The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.  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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score(X, y, sample_weight=None) [source]

Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.  Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.
sample_weightarray-like of shape (n_samples,), default=None

Sample weights.    Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

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.


sklearn.ensemble.ExtraTreesClassifier
class sklearn.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', 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=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None) [source]

An extra-trees classifier. 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. 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{“gini”, “entropy”}, default=”gini”

The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
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 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.
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 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.
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{“auto”, “sqrt”, “log2”}, int or float, default=”auto”

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 round(max_features * n_features) features are considered at each split. If “auto”, then max_features=sqrt(n_features). If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.  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, 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.  New in version 0.19.
min_impurity_splitfloat, default=None

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_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.
bootstrapbool, default=False

Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization accuracy.
n_jobsint, 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_stateint, RandomState instance or None, default=None

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.
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 the Glossary.
class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None

Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
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_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0, 1).   New in version 0.22.     Attributes

base_estimator_ExtraTreesClassifier

The child estimator template used to create the collection of fitted sub-estimators.
estimators_list of DecisionTreeClassifier

The collection of fitted sub-estimators.
classes_ndarray of shape (n_classes,) or a list of such arrays

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
n_classes_int or list

The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).
feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.
n_features_int

The number of features when fit is performed.
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_decision_function_ndarray of shape (n_samples, n_classes)

Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN. This attribute exists only when oob_score is True.      See also
sklearn.tree.ExtraTreeClassifier

Base classifier for this ensemble.
RandomForestClassifier

Ensemble Classifier based on trees with optimal splits.    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.ensemble import ExtraTreesClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0)
>>> clf.fit(X, y)
ExtraTreesClassifier(random_state=0)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
 Methods
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 class for X.
predict_log_proba(X) Predict class log-probabilities for X.
predict_proba(X) Predict class probabilities for X.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
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 sparse csr_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 sparse csr_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 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 sparse csc_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

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 class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.  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 sparse csr_matrix.    Returns

yndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes.

predict_log_proba(X) [source]

Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities 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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba(X) [source]

Predict class probabilities for X. The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.  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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score(X, y, sample_weight=None) [source]

Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.  Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.
sample_weightarray-like of shape (n_samples,), default=None

Sample weights.    Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

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.

 Examples using sklearn.ensemble.ExtraTreesClassifier

  Pixel importances with a parallel forest of trees

  Feature importances with forests of trees

  Hashing feature transformation using Totally Random Trees

  Plot the decision surfaces of ensembles of trees on the iris dataset


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 sparse csr_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 sparse csr_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 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 sparse csc_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


predict(X) [source]

Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.  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 sparse csr_matrix.    Returns

yndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes.


predict_log_proba(X) [source]

Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities 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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.


predict_proba(X) [source]

Predict class probabilities for X. The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.  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 sparse csr_matrix.    Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.


score(X, y, sample_weight=None) [source]

Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.  Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.
sample_weightarray-like of shape (n_samples,), default=None

Sample weights.    Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.


class sklearn.ensemble.ExtraTreesRegressor(n_estimators=100, *, 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=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None) [source]

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. 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{“mse”, “mae”}, 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_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 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.
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 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.
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{“auto”, “sqrt”, “log2”}, int or float, default=”auto”

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 round(max_features * n_features) features are considered at each split. If “auto”, then max_features=n_features. If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.  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, 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.  New in version 0.19.
min_impurity_splitfloat, default=None

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_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.
bootstrapbool, default=False

Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the R^2 on unseen data.
n_jobsint, 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_stateint, RandomState instance or None, default=None

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.
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 the Glossary.
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_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0, 1).   New in version 0.22.     Attributes

base_estimator_ExtraTreeRegressor

The child estimator template used to create the collection of fitted sub-estimators.
estimators_list of DecisionTreeRegressor

The collection of fitted sub-estimators.
feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.
n_features_int

The number of features.
n_outputs_int

The number of outputs.
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,)

Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score is True.      See also
sklearn.tree.ExtraTreeRegressor

Base estimator for this ensemble.
RandomForestRegressor

Ensemble regressor using trees with optimal splits.    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)
0.2708...
 Methods
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]) Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params) Set the parameters of this estimator.
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 sparse csr_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 sparse csr_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 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 sparse csc_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

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 sparse csr_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 \(R^2\) of the prediction. The coefficient \(R^2\) 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.  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), where n_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) wrt. 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).

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.


sklearn.ensemble.ExtraTreesRegressor
class sklearn.ensemble.ExtraTreesRegressor(n_estimators=100, *, 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=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None) [source]

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. 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{“mse”, “mae”}, 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_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 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.
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 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.
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{“auto”, “sqrt”, “log2”}, int or float, default=”auto”

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 round(max_features * n_features) features are considered at each split. If “auto”, then max_features=n_features. If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.  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, 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.  New in version 0.19.
min_impurity_splitfloat, default=None

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_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.
bootstrapbool, default=False

Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the R^2 on unseen data.
n_jobsint, 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_stateint, RandomState instance or None, default=None

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.
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 the Glossary.
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_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0, 1).   New in version 0.22.     Attributes

base_estimator_ExtraTreeRegressor

The child estimator template used to create the collection of fitted sub-estimators.
estimators_list of DecisionTreeRegressor

The collection of fitted sub-estimators.
feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.
n_features_int

The number of features.
n_outputs_int

The number of outputs.
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,)

Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score is True.      See also
sklearn.tree.ExtraTreeRegressor

Base estimator for this ensemble.
RandomForestRegressor

Ensemble regressor using trees with optimal splits.    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)
0.2708...
 Methods
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]) Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params) Set the parameters of this estimator.
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 sparse csr_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 sparse csr_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 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 sparse csc_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

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 sparse csr_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 \(R^2\) of the prediction. The coefficient \(R^2\) 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.  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), where n_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) wrt. 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).

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.

 Examples using sklearn.ensemble.ExtraTreesRegressor

  Face completion with a multi-output estimators

  Imputing missing values with variants of IterativeImputer


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 sparse csr_matrix.    Returns

yndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted values.