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.  New in version 0.18.   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. 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. None means 1 unless in a joblib.parallel_backend context. -1 means using all available processes / threads. See Glossary for more details.  Changed in version 0.20: n_jobs default changed from 1 to None     Attributes 
 
estimators_list of n_output estimators 

Estimators used for predictions.     Examples >>> import numpy as np
>>> from sklearn.datasets import load_linnerud
>>> from sklearn.multioutput import MultiOutputRegressor
>>> from sklearn.linear_model import Ridge
>>> X, y = load_linnerud(return_X_y=True)
>>> clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
>>> clf.predict(X[[0]])
array([[176..., 35..., 57...]])
 Methods  
fit(X, y[, sample_weight]) Fit the model to data.  
get_params([deep]) Get parameters for this estimator.  
partial_fit(X, y[, sample_weight]) Incrementally fit the model to data.  
predict(X) Predict multi-output variable using a model  
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y, sample_weight=None, **fit_params) [source]
 
Fit the model to data. Fit a separate model for each output variable.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Data.  
y{array-like, sparse matrix} of shape (n_samples, n_outputs) 

Multi-output targets. An indicator matrix turns on multilabel estimation.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.  
**fit_paramsdict of string -> object 

Parameters passed to the estimator.fit method of each step.  New in version 0.23.     Returns 
 
selfobject 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

Parameter names mapped to their values.     
  
partial_fit(X, y, sample_weight=None) [source]
 
Incrementally fit the model to data. Fit a separate model for each output variable.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Data.  
y{array-like, sparse matrix} of shape (n_samples, n_outputs) 

Multi-output targets.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.    Returns 
 
selfobject 
   
  
predict(X) [source]
 
 Predict multi-output variable using a model

trained for each target variable.    Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Data.    Returns 
 
y{array-like, sparse matrix} of 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) [source]
 
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

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

True values for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

\(R^2\) of self.predict(X) wrt. y.     Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). 
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
 
 Examples using sklearn.multioutput.MultiOutputRegressor
 
  Comparing random forests and the multi-output meta estimator