sklearn.multioutput.MultiOutputRegressor

class sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=None)[source]

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.

Parameters
estimatorestimator object

An estimator object implementing fit and predict.

n_jobsint or None, optional (default=None)

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

When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.

Attributes
estimators_list of n_output estimators

Estimators used for predictions.

Methods

fit(self, X, y[, sample_weight])

Fit the model to data.

get_params(self[, deep])

Get parameters for this estimator.

partial_fit(self, X, y[, sample_weight])

Incrementally fit the model to data.

predict(self, X)

Predict multi-output variable using a model

score(self, X, y[, sample_weight])

Returns the coefficient of determination R^2 of the prediction.

set_params(self, \*\*params)

Set the parameters of this estimator.

__init__(self, estimator, n_jobs=None)[source]

Initialize self. See help(type(self)) for accurate signature.

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

Fit the model to data. Fit a separate model for each output variable.

Parameters
X(sparse) array-like, shape (n_samples, n_features)

Data.

y(sparse) array-like, shape (n_samples, n_outputs)

Multi-output targets. An indicator matrix turns on multilabel estimation.

sample_weightarray-like, shape = (n_samples) or None

Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.

Returns
selfobject
get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y, sample_weight=None)[source]

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters
X(sparse) array-like, shape (n_samples, n_features)

Data.

y(sparse) array-like, shape (n_samples, n_outputs)

Multi-output targets.

sample_weightarray-like, shape = (n_samples) or None

Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.

Returns
selfobject
predict(self, X)[source]
Predict multi-output variable using a model

trained for each target variable.

Parameters
X(sparse) array-like, shape (n_samples, n_features)

Data.

Returns
y(sparse) array-like, shape (n_samples, n_outputs)

Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.

score(self, X, y, sample_weight=None)[source]

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the regression sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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, shape (n_samples, n_features)

Test samples.

yarray-like, shape (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like, shape [n_samples], optional

Sample weights.

Returns
scorefloat

R^2 of self.predict(X) wrt. y.

Notes

R^2 is calculated by weighting all the targets equally using multioutput='uniform_average'.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns
self

Examples using sklearn.multioutput.MultiOutputRegressor