sklearn.multioutput.MultiOutputClassifier

class sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=1) [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, optional, default=1 The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. The number of jobs to use for the computation. It does each target variable in y in parallel.

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.
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=1) [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 Returns self.

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.

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 probabilites for each class of each output.

Parameters:	X : array-like, shape (n_samples, n_features) Data
Returns:	T : (sparse) array-like, shape = (n_samples, n_classes, n_outputs) The class probabilities of the samples for each of the outputs

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 :