sklearn.multioutput.MultiOutputClassifier  
class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None) [source]
 
Multi target classification This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification  Parameters 
 
estimatorestimator object 

An estimator object implementing fit, score and predict_proba.  
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 
 
classes_ndarray of shape (n_classes,) 

Class labels.  
estimators_list of n_output estimators 

Estimators used for predictions.     Examples >>> import numpy as np
>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.multioutput import MultiOutputClassifier
>>> from sklearn.neighbors import KNeighborsClassifier
 >>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
>>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y)
>>> clf.predict(X[-2:])
array([[1, 1, 0], [1, 1, 1]])
 Methods  
fit(X, Y[, sample_weight]) Fit the model to data matrix X and targets Y.  
get_params([deep]) Get parameters for this estimator.  
partial_fit(X, y[, classes, sample_weight]) Incrementally fit the model to data.  
predict(X) Predict multi-output variable using a model  
score(X, y) Returns the mean accuracy on the given test data and labels.  
set_params(**params) Set the parameters of this estimator.    
fit(X, Y, sample_weight=None, **fit_params) [source]
 
Fit the model to data matrix X and targets Y.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The input data.  
Yarray-like of shape (n_samples, n_classes) 

The target values.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights. If None, then samples are equally weighted. Only supported if the underlying classifier 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, classes=None, 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.  
classeslist of ndarray of shape (n_outputs,) 

Each array is unique classes for one output in str/int Can be obtained by via [np.unique(y[:, i]) for i in range(y.shape[1])], where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.  
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.     
  
property predict_proba  
Probability estimates. Returns prediction probabilities for each class of each output. This method will raise a ValueError if any of the estimators do not have predict_proba.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Data    Returns 
 
parray of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1. 

The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.  Changed in version 0.19: This function now returns a list of arrays where the length of the list is n_outputs, and each array is (n_samples, n_classes) for that particular output.      
  
score(X, y) [source]
 
Returns the mean accuracy on the given test data and labels.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples  
yarray-like of shape (n_samples, n_outputs) 

True values for X    Returns 
 
scoresfloat 

accuracy_score of self.predict(X) versus y     
  
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.