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:	estimator : estimator object An estimator object implementing fit, score and predict_proba. n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. It does each target variable in y in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Attributes:	estimators_ : list of n_output estimators Estimators used for predictions.

Methods

fit(X, y[, sample_weight])	Fit the model to data.
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 trained for each target variable.
predict_proba(X)	Probability estimates.
score(X, y)	“Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

__init__(estimator, n_jobs=None) [source]

fit(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_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

get_params(deep=True) [source]

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.

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 : (sparse) array-like, shape (n_samples, n_features) Data. y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets. classes : list of numpy arrays, 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_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

predict(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.

predict_proba(X) [source]

Probability estimates. Returns prediction probabilities for each class of each output.

Parameters:	X : array-like, shape (n_samples, n_features) Data
Returns:	p : array 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_.

score(X, y) [source]

“Returns the mean accuracy on the given test data and labels.

Parameters:	X : array-like, shape [n_samples, n_features] Test samples y : array-like, shape [n_samples, n_outputs] True values for X
Returns:	scores : float 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 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