sklearn.ensemble
.StackingRegressor¶
- 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 fullX
whilefinal_estimator_
is trained using cross-validated predictions of the base estimators usingcross_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, iterable, or “prefit”, default=None
Determines the cross-validation splitting strategy used in
cross_val_predict
to trainfinal_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.
“prefit” to assume the
estimators
are prefit, and skip cross validation
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. These splitters are instantiated withshuffle=False
so the splits will be the same across calls.Refer User Guide for the various cross-validation strategies that can be used here.
If “prefit” is passed, it is assumed that all
estimators
have been fitted already. Thefinal_estimator_
is trained on theestimators
predictions on the full training set and are not cross validated predictions. Please note that if the models have been trained on the same data to train the stacking model, there is a very high risk of overfitting.New in version 1.1: The ‘prefit’ option was added in 1.1
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 allestimators
.None
means 1 unless in ajoblib.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, thefinal_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 inestimators_
. Whencv="prefit"
,estimators_
is set toestimators
and is not fitted again.- named_estimators_
Bunch
Attribute to access any fitted sub-estimators by name.
n_features_in_
intNumber of features seen during fit.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit. .. versionadded:: 1.0
- final_estimator_estimator
The regressor to stacked the base estimators fitted.
- stack_method_list of str
The method used by each base estimator.
See also
StackingClassifier
Stack of estimators with a final classifier.
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_feature_names_out
([input_features])Get output feature names for transformation.
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 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 andn_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
Returns a fitted instance.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features. The input feature names are only used when
passthrough
isTrue
.If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then names are generated:[x0, x1, ..., x(n_features_in_ - 1)]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
If
passthrough
isFalse
, then only the names ofestimators
are used to generate the output feature names.
- Returns:
- feature_names_outndarray of str objects
Transformed feature names.
- 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.
- Returns:
- paramsdict
Parameter and estimator names mapped to their values or parameter names mapped to their values.
- 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 andn_features
is the number of features.- **predict_paramsdict of str -> obj
Parameters to the
predict
called by thefinal_estimator
. Note that this may be used to return uncertainties from some estimators withreturn_std
orreturn_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 of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred)** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
wrt.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_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 inestimators
.- 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’.
- Returns:
- selfobject
Estimator instance.
- 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 andn_features
is the number of features.
- Returns:
- y_predsndarray of shape (n_samples, n_estimators)
Prediction outputs for each estimator.
Examples using sklearn.ensemble.StackingRegressor
¶
Combine predictors using stacking