Documentation
Classes
VotingRegressor

VotingRegressor

Prediction voting regressor for unfitted estimators.

A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new VotingRegressor(opts?: object): VotingRegressor;

Parameters

NameTypeDescription
opts?object-
opts.estimators?anyInvoking the fit method on the VotingRegressor will fit clones of those original estimators that will be stored in the class attribute self.estimators\_. An estimator can be set to 'drop' using set\_params.
opts.n_jobs?numberThe number of jobs to run in parallel for fit. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.verbose?booleanIf true, the time elapsed while fitting will be printed as it is completed. Default Value false
opts.weights?ArrayLikeSequence of weights (float or int) to weight the occurrences of predicted values before averaging. Uses uniform weights if undefined.

Returns

VotingRegressor

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

Defined in: generated/ensemble/VotingRegressor.ts:22 (opens in a new tab)

_py

PythonBridge

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

id

string

Defined in: generated/ensemble/VotingRegressor.ts:18 (opens in a new tab)

opts

any

Defined in: generated/ensemble/VotingRegressor.ts:19 (opens in a new tab)

Accessors

estimators_

The collection of fitted sub-estimators as defined in estimators that are not ‘drop’.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/VotingRegressor.ts:410 (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/VotingRegressor.ts:460 (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/VotingRegressor.ts:435 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/ensemble/VotingRegressor.ts:52 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/ensemble/VotingRegressor.ts:56 (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/VotingRegressor.ts:109 (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/VotingRegressor.ts:126 (opens in a new tab)

fit_transform()

Return class labels or probabilities for each estimator.

Return predictions for X for each estimator.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeInput samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/ensemble/VotingRegressor.ts:175 (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?anyNot used, present here for API consistency by convention.

Returns

Promise<any>

Defined in: generated/ensemble/VotingRegressor.ts:222 (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/VotingRegressor.ts:65 (opens in a new tab)

predict()

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe input samples.

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/VotingRegressor.ts:260 (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/VotingRegressor.ts:295 (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/VotingRegressor.ts:344 (opens in a new tab)

transform()

Return predictions for X for each estimator.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/ensemble/VotingRegressor.ts:377 (opens in a new tab)