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MultiOutputRegressor

Multi target regression.

This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

Python Reference

Constructors

constructor()

Signature

new MultiOutputRegressor(opts?: object): MultiOutputRegressor;

Parameters

Name Type Description
opts? object -
opts.estimator? any An estimator object implementing fit and predict.
opts.n_jobs? number The number of jobs to run in parallel. fit, predict and partial\_fit (if supported by the passed estimator) will be parallelized for each target. When individual estimators are fast to train or predict, using n\_jobs > 1 can result in slower performance due to the parallelism overhead. undefined means 1 unless in a joblib.parallel\_backend context. \-1 means using all available processes / threads. See Glossary for more details.

Returns

MultiOutputRegressor

Defined in: generated/multioutput/MultiOutputRegressor.ts:23

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/multioutput/MultiOutputRegressor.ts:99

fit()

Fit the model to data, separately for each output variable.

Signature

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

Parameters

Name Type Description
opts object -
opts.X? ArrayLike The input data.
opts.fit_params? any Parameters passed to the estimator.fit method of each step.
opts.sample_weight? ArrayLike Sample weights. If undefined, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
opts.y? ArrayLike Multi-output targets. An indicator matrix turns on multilabel estimation.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:116

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

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

Parameters

Name Type Description
opts object -
opts.routing? any A MetadataRouter encapsulating routing information.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:174

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

Name Type
py PythonBridge

Returns

Promise<void>

Defined in: generated/multioutput/MultiOutputRegressor.ts:55

partial_fit()

Incrementally fit the model to data, for each output variable.

Signature

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

Parameters

Name Type Description
opts object -
opts.X? ArrayLike The input data.
opts.partial_fit_params? any Parameters passed to the estimator.partial\_fit method of each sub-estimator. Only available if enable\_metadata\_routing=True. See the User Guide.
opts.sample_weight? ArrayLike Sample weights. If undefined, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
opts.y? ArrayLike Multi-output targets.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:212

predict()

Predict multi-output variable using model for each target variable.

Signature

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

Parameters

Name Type Description
opts object -
opts.X? ArrayLike The input data.

Returns

Promise<ArrayLike>

Defined in: generated/multioutput/MultiOutputRegressor.ts:274

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

Name Type Description
opts object -
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? ArrayLike Sample weights.
opts.y? ArrayLike True values for X.

Returns

Promise<number>

Defined in: generated/multioutput/MultiOutputRegressor.ts:311

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name Type Description
opts object -
opts.sample_weight? string | boolean Metadata routing for sample\_weight parameter in fit.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:364

set_partial_fit_request()

Request metadata passed to the partial\_fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name Type Description
opts object -
opts.sample_weight? string | boolean Metadata routing for sample\_weight parameter in partial\_fit.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:406

set_score_request()

Request metadata passed to the score method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

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

Parameters

Name Type Description
opts object -
opts.sample_weight? string | boolean Metadata routing for sample\_weight parameter in score.

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:448

Properties

_isDisposed

boolean = false

Defined in: generated/multioutput/MultiOutputRegressor.ts:21

_isInitialized

boolean = false

Defined in: generated/multioutput/MultiOutputRegressor.ts:20

_py

PythonBridge

Defined in: generated/multioutput/MultiOutputRegressor.ts:19

id

string

Defined in: generated/multioutput/MultiOutputRegressor.ts:16

opts

any

Defined in: generated/multioutput/MultiOutputRegressor.ts:17

Accessors

estimators_

Estimators used for predictions.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/multioutput/MultiOutputRegressor.ts:486

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/multioutput/MultiOutputRegressor.ts:540

n_features_in_

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/multioutput/MultiOutputRegressor.ts:513

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/multioutput/MultiOutputRegressor.ts:42

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name Type
pythonBridge PythonBridge

Returns

void

Defined in: generated/multioutput/MultiOutputRegressor.ts:46