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.


class sklearn.ensemble.RandomForestRegressor(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=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None) [source]

A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree. 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=True

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 both the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True) and the sampling of the features to consider when looking for the best split at each node (if max_features < n_features). See Glossary for details.
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_DecisionTreeRegressor

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 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_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
DecisionTreeRegressor, ExtraTreesRegressor

  Notes The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features and bootstrap=False, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state has to be fixed. The default value max_features="auto" uses n_features rather than n_features / 3. The latter was originally suggested in [1], whereas the former was more recently justified empirically in [2]. References
1
 Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
2
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.   Examples >>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
...                        random_state=0, shuffle=False)
>>> regr = RandomForestRegressor(max_depth=2, random_state=0)
>>> regr.fit(X, y)
RandomForestRegressor(...)
>>> print(regr.predict([[0, 0, 0, 0]]))
[-8.32987858]
 Methods
apply(X) Apply trees in the forest to X, return leaf indices.
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.RandomForestRegressor
class sklearn.ensemble.RandomForestRegressor(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=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None) [source]

A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree. 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=True

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 both the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True) and the sampling of the features to consider when looking for the best split at each node (if max_features < n_features). See Glossary for details.
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_DecisionTreeRegressor

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 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_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
DecisionTreeRegressor, ExtraTreesRegressor

  Notes The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features and bootstrap=False, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state has to be fixed. The default value max_features="auto" uses n_features rather than n_features / 3. The latter was originally suggested in [1], whereas the former was more recently justified empirically in [2]. References
1
 Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
2
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.   Examples >>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
...                        random_state=0, shuffle=False)
>>> regr = RandomForestRegressor(max_depth=2, random_state=0)
>>> regr.fit(X, y)
RandomForestRegressor(...)
>>> print(regr.predict([[0, 0, 0, 0]]))
[-8.32987858]
 Methods
apply(X) Apply trees in the forest to X, return leaf indices.
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.RandomForestRegressor

  Release Highlights for scikit-learn 0.24

  Plot individual and voting regression predictions

  Comparing random forests and the multi-output meta estimator

  Combine predictors using stacking

  Prediction Latency

  Imputing missing values before building an 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.


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).


class sklearn.ensemble.RandomTreesEmbedding(n_estimators=100, *, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False) [source]

An ensemble of totally random trees. An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest. The dimensionality of the resulting representation is n_out <= n_estimators * max_leaf_nodes. If max_leaf_nodes == None, the number of leaf nodes is at most n_estimators * 2 ** max_depth. Read more in the User Guide.  Parameters

n_estimatorsint, default=100

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.
max_depthint, default=5

The maximum depth of each 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) is 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) is 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_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.
sparse_outputbool, default=True

Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.
n_jobsint, default=None

The number of jobs to run in parallel. fit, transform, 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 the generation of the random y used to fit the trees and the draw of the splits for each feature at the trees’ nodes. 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.    Attributes

base_estimator_DecisionTreeClassifier instance

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

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 when fit is performed.
n_outputs_int

The number of outputs when fit is performed.
one_hot_encoder_OneHotEncoder instance

One-hot encoder used to create the sparse embedding.     References
1
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
2
Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007   Examples >>> from sklearn.ensemble import RandomTreesEmbedding
>>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
>>> random_trees = RandomTreesEmbedding(
...    n_estimators=5, random_state=0, max_depth=1).fit(X)
>>> X_sparse_embedding = random_trees.transform(X)
>>> X_sparse_embedding.toarray()
array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
       [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
       [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
       [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])
 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]) Fit estimator.
fit_transform(X[, y, sample_weight]) Fit estimator and transform dataset.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Transform 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=None, sample_weight=None) [source]

Fit estimator.  Parameters

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

The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc_matrix for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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

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

Fit estimator and transform dataset.  Parameters

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

Input data used to build forests. Use dtype=np.float32 for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.

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.

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.

transform(X) [source]

Transform dataset.  Parameters

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

Input data to be transformed. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csr_matrix for maximum efficiency.    Returns

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.


sklearn.ensemble.RandomTreesEmbedding
class sklearn.ensemble.RandomTreesEmbedding(n_estimators=100, *, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False) [source]

An ensemble of totally random trees. An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest. The dimensionality of the resulting representation is n_out <= n_estimators * max_leaf_nodes. If max_leaf_nodes == None, the number of leaf nodes is at most n_estimators * 2 ** max_depth. Read more in the User Guide.  Parameters

n_estimatorsint, default=100

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.
max_depthint, default=5

The maximum depth of each 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) is 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) is 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_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.
sparse_outputbool, default=True

Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.
n_jobsint, default=None

The number of jobs to run in parallel. fit, transform, 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 the generation of the random y used to fit the trees and the draw of the splits for each feature at the trees’ nodes. 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.    Attributes

base_estimator_DecisionTreeClassifier instance

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

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 when fit is performed.
n_outputs_int

The number of outputs when fit is performed.
one_hot_encoder_OneHotEncoder instance

One-hot encoder used to create the sparse embedding.     References
1
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
2
Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007   Examples >>> from sklearn.ensemble import RandomTreesEmbedding
>>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
>>> random_trees = RandomTreesEmbedding(
...    n_estimators=5, random_state=0, max_depth=1).fit(X)
>>> X_sparse_embedding = random_trees.transform(X)
>>> X_sparse_embedding.toarray()
array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
       [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
       [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
       [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
       [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])
 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]) Fit estimator.
fit_transform(X[, y, sample_weight]) Fit estimator and transform dataset.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Transform 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=None, sample_weight=None) [source]

Fit estimator.  Parameters

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

The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc_matrix for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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

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

Fit estimator and transform dataset.  Parameters

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

Input data used to build forests. Use dtype=np.float32 for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.

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.

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.

transform(X) [source]

Transform dataset.  Parameters

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

Input data to be transformed. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csr_matrix for maximum efficiency.    Returns

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.

 Examples using sklearn.ensemble.RandomTreesEmbedding

  Hashing feature transformation using Totally Random Trees

  Feature transformations with ensembles of trees

  Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…


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

Fit estimator.  Parameters

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

The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc_matrix for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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


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

Fit estimator and transform dataset.  Parameters

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

Input data used to build forests. Use dtype=np.float32 for maximum efficiency.
yIgnored

Not used, present for API consistency by convention.
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

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.


transform(X) [source]

Transform dataset.  Parameters

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

Input data to be transformed. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csr_matrix for maximum efficiency.    Returns

X_transformedsparse matrix of shape (n_samples, n_out)

Transformed dataset.


class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0) [source]

Stack of estimators with a final classifier. Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator. Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict. Read more in the User Guide.  New in version 0.22.   Parameters

estimatorslist of (str, estimator)

Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
final_estimatorestimator, default=None

A classifier which will be used to combine the base estimators. The default classifier is a LogisticRegression.
cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator. Possible inputs for cv are:  None, to use the default 5-fold cross validation, integer, to specify the number of folds in a (Stratified) KFold, An object to be used as a cross-validation generator, An iterable yielding train, test splits.  For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here.  Note A larger number of split will provide no benefits if the number of training samples is large enough. Indeed, the training time will increase. cv is not used for model evaluation but for prediction.
stack_method{‘auto’, ‘predict_proba’, ‘decision_function’, ‘predict’}, default=’auto’

Methods called for each base estimator. It can be:  if ‘auto’, it will try to invoke, for each estimator, 'predict_proba', 'decision_function' or 'predict' in that order. otherwise, one of 'predict_proba', 'decision_function' or 'predict'. If the method is not implemented by the estimator, it will raise an error.
n_jobsint, default=None

The number of jobs to run in parallel all estimators fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
passthroughbool, default=False

When False, only the predictions of estimators will be used as training data for final_estimator. When True, the final_estimator is trained on the predictions as well as the original training data.
verboseint, default=0

Verbosity level.    Attributes

classes_ndarray of shape (n_classes,)

Class labels.
estimators_list of estimators

The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to 'drop', it will not appear in estimators_.
named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.
final_estimator_estimator

The classifier which predicts given the output of estimators_.
stack_method_list of str

The method used by each base estimator.     Notes When predict_proba is used by each estimator (i.e. most of the time for stack_method='auto' or specifically for stack_method='predict_proba'), The first column predicted by each estimator will be dropped in the case of a binary classification problem. Indeed, both feature will be perfectly collinear. References
1
Wolpert, David H. “Stacked generalization.” Neural networks 5.2 (1992): 241-259.   Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.svm import LinearSVC
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.ensemble import StackingClassifier
>>> X, y = load_iris(return_X_y=True)
>>> estimators = [
...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
...     ('svr', make_pipeline(StandardScaler(),
...                           LinearSVC(random_state=42)))
... ]
>>> clf = StackingClassifier(
...     estimators=estimators, final_estimator=LogisticRegression()
... )
>>> from sklearn.model_selection import train_test_split
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, stratify=y, random_state=42
... )
>>> clf.fit(X_train, y_train).score(X_test, y_test)
0.9...
 Methods
decision_function(X) Predict decision function for samples in X using final_estimator_.decision_function.
fit(X, y[, sample_weight]) Fit the estimators.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get the parameters of an estimator from the ensemble.
predict(X, **predict_params) Predict target for X.
predict_proba(X) Predict class probabilities for X using final_estimator_.predict_proba.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of an estimator from the ensemble.
transform(X) Return class labels or probabilities for X for each estimator.
decision_function(X) [source]

Predict decision function for samples in X using final_estimator_.decision_function.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

decisionsndarray of shape (n_samples,), (n_samples, n_classes), or (n_samples, n_classes * (n_classes-1) / 2)

The decision function computed the final estimator.

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

Fit the estimators.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)

Target values.
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 all underlying estimators support sample weights.    Returns

selfobject

fit_transform(X, y=None, **fit_params) [source]

Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).
**fit_paramsdict

Additional fit parameters.    Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True) [source]

Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.  Parameters

deepbool, default=True

Setting it to True gets the various estimators and the parameters of the estimators as well.

property n_features_in_
Number of features seen during fit.

predict(X, **predict_params) [source]

Predict target for X.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj

Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator.    Returns

y_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

predict_proba(X) [source]

Predict class probabilities for X using final_estimator_.predict_proba.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

probabilitiesndarray of shape (n_samples, n_classes) or list of ndarray of shape (n_output,)

The class probabilities of the input samples.

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 an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.  Parameters

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

transform(X) [source]

Return class labels or probabilities for X for each estimator.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

y_predsndarray of shape (n_samples, n_estimators) or (n_samples, n_classes * n_estimators)

Prediction outputs for each estimator.


sklearn.ensemble.StackingClassifier
class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0) [source]

Stack of estimators with a final classifier. Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator. Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict. Read more in the User Guide.  New in version 0.22.   Parameters

estimatorslist of (str, estimator)

Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
final_estimatorestimator, default=None

A classifier which will be used to combine the base estimators. The default classifier is a LogisticRegression.
cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator. Possible inputs for cv are:  None, to use the default 5-fold cross validation, integer, to specify the number of folds in a (Stratified) KFold, An object to be used as a cross-validation generator, An iterable yielding train, test splits.  For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here.  Note A larger number of split will provide no benefits if the number of training samples is large enough. Indeed, the training time will increase. cv is not used for model evaluation but for prediction.
stack_method{‘auto’, ‘predict_proba’, ‘decision_function’, ‘predict’}, default=’auto’

Methods called for each base estimator. It can be:  if ‘auto’, it will try to invoke, for each estimator, 'predict_proba', 'decision_function' or 'predict' in that order. otherwise, one of 'predict_proba', 'decision_function' or 'predict'. If the method is not implemented by the estimator, it will raise an error.
n_jobsint, default=None

The number of jobs to run in parallel all estimators fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
passthroughbool, default=False

When False, only the predictions of estimators will be used as training data for final_estimator. When True, the final_estimator is trained on the predictions as well as the original training data.
verboseint, default=0

Verbosity level.    Attributes

classes_ndarray of shape (n_classes,)

Class labels.
estimators_list of estimators

The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to 'drop', it will not appear in estimators_.
named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.
final_estimator_estimator

The classifier which predicts given the output of estimators_.
stack_method_list of str

The method used by each base estimator.     Notes When predict_proba is used by each estimator (i.e. most of the time for stack_method='auto' or specifically for stack_method='predict_proba'), The first column predicted by each estimator will be dropped in the case of a binary classification problem. Indeed, both feature will be perfectly collinear. References
1
Wolpert, David H. “Stacked generalization.” Neural networks 5.2 (1992): 241-259.   Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.svm import LinearSVC
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.ensemble import StackingClassifier
>>> X, y = load_iris(return_X_y=True)
>>> estimators = [
...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
...     ('svr', make_pipeline(StandardScaler(),
...                           LinearSVC(random_state=42)))
... ]
>>> clf = StackingClassifier(
...     estimators=estimators, final_estimator=LogisticRegression()
... )
>>> from sklearn.model_selection import train_test_split
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, stratify=y, random_state=42
... )
>>> clf.fit(X_train, y_train).score(X_test, y_test)
0.9...
 Methods
decision_function(X) Predict decision function for samples in X using final_estimator_.decision_function.
fit(X, y[, sample_weight]) Fit the estimators.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get the parameters of an estimator from the ensemble.
predict(X, **predict_params) Predict target for X.
predict_proba(X) Predict class probabilities for X using final_estimator_.predict_proba.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of an estimator from the ensemble.
transform(X) Return class labels or probabilities for X for each estimator.
decision_function(X) [source]

Predict decision function for samples in X using final_estimator_.decision_function.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

decisionsndarray of shape (n_samples,), (n_samples, n_classes), or (n_samples, n_classes * (n_classes-1) / 2)

The decision function computed the final estimator.

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

Fit the estimators.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)

Target values.
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 all underlying estimators support sample weights.    Returns

selfobject

fit_transform(X, y=None, **fit_params) [source]

Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).
**fit_paramsdict

Additional fit parameters.    Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True) [source]

Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.  Parameters

deepbool, default=True

Setting it to True gets the various estimators and the parameters of the estimators as well.

property n_features_in_
Number of features seen during fit.

predict(X, **predict_params) [source]

Predict target for X.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj

Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator.    Returns

y_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

predict_proba(X) [source]

Predict class probabilities for X using final_estimator_.predict_proba.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

probabilitiesndarray of shape (n_samples, n_classes) or list of ndarray of shape (n_output,)

The class probabilities of the input samples.

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 an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.  Parameters

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

transform(X) [source]

Return class labels or probabilities for X for each estimator.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

y_predsndarray of shape (n_samples, n_estimators) or (n_samples, n_classes * n_estimators)

Prediction outputs for each estimator.

 Examples using sklearn.ensemble.StackingClassifier

  Release Highlights for scikit-learn 0.22


decision_function(X) [source]

Predict decision function for samples in X using final_estimator_.decision_function.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

decisionsndarray of shape (n_samples,), (n_samples, n_classes), or (n_samples, n_classes * (n_classes-1) / 2)

The decision function computed the final estimator.


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

Fit the estimators.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)

Target values.
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 all underlying estimators support sample weights.    Returns

selfobject


fit_transform(X, y=None, **fit_params) [source]

Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).
**fit_paramsdict

Additional fit parameters.    Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


get_params(deep=True) [source]

Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.  Parameters

deepbool, default=True

Setting it to True gets the various estimators and the parameters of the estimators as well.


property n_features_in_
Number of features seen during fit.


predict(X, **predict_params) [source]

Predict target for X.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj

Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator.    Returns

y_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.


predict_proba(X) [source]

Predict class probabilities for X using final_estimator_.predict_proba.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

probabilitiesndarray of shape (n_samples, n_classes) or list of ndarray of shape (n_output,)

The class probabilities of the input samples.


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 an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.  Parameters

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.


transform(X) [source]

Return class labels or probabilities for X for each estimator.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

y_predsndarray of shape (n_samples, n_estimators) or (n_samples, n_classes * n_estimators)

Prediction outputs for each estimator.


class sklearn.ensemble.StackingRegressor(estimators, final_estimator=None, *, cv=None, n_jobs=None, passthrough=False, verbose=0) [source]

Stack of estimators with a final regressor. Stacked generalization consists in stacking the output of individual estimator and use a regressor to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator. Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict. Read more in the User Guide.  New in version 0.22.   Parameters

estimatorslist of (str, estimator)

Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
final_estimatorestimator, default=None

A regressor which will be used to combine the base estimators. The default regressor is a RidgeCV.
cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator. Possible inputs for cv are:  None, to use the default 5-fold cross validation, integer, to specify the number of folds in a (Stratified) KFold, An object to be used as a cross-validation generator, An iterable yielding train, test splits.  For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here.  Note A larger number of split will provide no benefits if the number of training samples is large enough. Indeed, the training time will increase. cv is not used for model evaluation but for prediction.
n_jobsint, default=None

The number of jobs to run in parallel for fit of all estimators. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
passthroughbool, default=False

When False, only the predictions of estimators will be used as training data for final_estimator. When True, the final_estimator is trained on the predictions as well as the original training data.
verboseint, default=0

Verbosity level.    Attributes

estimators_list of estimator

The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to 'drop', it will not appear in estimators_.
named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.
final_estimator_estimator

The regressor to stacked the base estimators fitted.     References
1
Wolpert, David H. “Stacked generalization.” Neural networks 5.2 (1992): 241-259.   Examples >>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import RidgeCV
>>> from sklearn.svm import LinearSVR
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.ensemble import StackingRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> estimators = [
...     ('lr', RidgeCV()),
...     ('svr', LinearSVR(random_state=42))
... ]
>>> reg = StackingRegressor(
...     estimators=estimators,
...     final_estimator=RandomForestRegressor(n_estimators=10,
...                                           random_state=42)
... )
>>> from sklearn.model_selection import train_test_split
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=42
... )
>>> reg.fit(X_train, y_train).score(X_test, y_test)
0.3...
 Methods
fit(X, y[, sample_weight]) Fit the estimators.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get the parameters of an estimator from the ensemble.
predict(X, **predict_params) Predict 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 an estimator from the ensemble.
transform(X) Return the predictions for X for each estimator.
fit(X, y, sample_weight=None) [source]

Fit the estimators.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)

Target values.
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 all underlying estimators support sample weights.    Returns

selfobject

fit_transform(X, y=None, **fit_params) [source]

Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).
**fit_paramsdict

Additional fit parameters.    Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True) [source]

Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.  Parameters

deepbool, default=True

Setting it to True gets the various estimators and the parameters of the estimators as well.

property n_features_in_
Number of features seen during fit.

predict(X, **predict_params) [source]

Predict target for X.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj

Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator.    Returns

y_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

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 an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.  Parameters

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

transform(X) [source]

Return the predictions for X for each estimator.  Parameters

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.    Returns

y_predsndarray of shape (n_samples, n_estimators)

Prediction outputs for each estimator.