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Classes
StackingRegressor

StackingRegressor

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.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new StackingRegressor(opts?: object): StackingRegressor;

Parameters

NameTypeDescription
opts?object-
opts.cv?number | "prefit"Determines the cross-validation splitting strategy used in cross\_val\_predict to train final\_estimator. Possible inputs for cv are:
opts.estimators?anyBase 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.
opts.final_estimator?anyA regressor which will be used to combine the base estimators. The default regressor is a RidgeCV.
opts.n_jobs?numberThe number of jobs to run in parallel for fit of all estimators. undefined means 1 unless in a joblib.parallel\_backend context. -1 means using all processors. See Glossary for more details.
opts.passthrough?booleanWhen 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. Default Value false
opts.verbose?numberVerbosity level. Default Value 0

Returns

StackingRegressor

Defined in: generated/ensemble/StackingRegressor.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/ensemble/StackingRegressor.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/ensemble/StackingRegressor.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/ensemble/StackingRegressor.ts:23 (opens in a new tab)

id

string

Defined in: generated/ensemble/StackingRegressor.ts:20 (opens in a new tab)

opts

any

Defined in: generated/ensemble/StackingRegressor.ts:21 (opens in a new tab)

Accessors

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\_. When cv="prefit", estimators\_ is set to estimators and is not fitted again.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:445 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/StackingRegressor.ts:499 (opens in a new tab)

final_estimator_

The regressor to stacked the base estimators fitted.

Signature

final_estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:526 (opens in a new tab)

named_estimators_

Attribute to access any fitted sub-estimators by name.

Signature

named_estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:472 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/ensemble/StackingRegressor.ts:66 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/ensemble/StackingRegressor.ts:70 (opens in a new tab)

stack_method_

The method used by each base estimator.

Signature

stack_method_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:553 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/ensemble/StackingRegressor.ts:125 (opens in a new tab)

fit()

Fit the estimators.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features.
opts.sample_weight?ArrayLikeSample weights. If undefined, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:142 (opens in a new tab)

fit_transform()

Fit the estimators and return the predictions for X for each estimator.

Signature

fit_transform(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features.
opts.sample_weight?ArrayLikeSample weights. If undefined, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.
opts.y?ArrayLikeTarget values.

Returns

Promise<ArrayLike[]>

Defined in: generated/ensemble/StackingRegressor.ts:191 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.input_features?anyInput features. The input feature names are only used when passthrough is true.

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:242 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/ensemble/StackingRegressor.ts:79 (opens in a new tab)

predict()

Predict target for X.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features.
opts.predict_params?anyParameters 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

Promise<ArrayLike>

Defined in: generated/ensemble/StackingRegressor.ts:280 (opens in a new tab)

score()

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 of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]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.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/ensemble/StackingRegressor.ts:324 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/ensemble/StackingRegressor.ts:375 (opens in a new tab)

transform()

Return the predictions for X for each estimator.

Signature

transform(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features.

Returns

Promise<ArrayLike[]>

Defined in: generated/ensemble/StackingRegressor.ts:410 (opens in a new tab)