sklearn.multioutput.MultiOutputRegressor

class sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=1)

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:

estimator : estimator object

An estimator object implementing fit and predict.

n_jobs : int, optional, default=1

The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores. 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.

Methods

fit(X, y[, sample_weight])	Fit the model to data.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict multi-output variable using a model trained for each target variable.
score(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(\*\*params)	Set the parameters of this estimator.

__init__(estimator, n_jobs=1)

fit(X, y, sample_weight=None)

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_weight : array-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:

self : object

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

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(X, y, sample_weight=None)

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

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

X : array-like, shape (n_samples, n_features)

Test samples.

y : array-like, shape (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like, shape [n_samples], optional

Sample weights.

Returns:

score : float

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(**params)

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

Comparing random forests and the multi-output meta estimator