sklearn.multioutput.MultiOutputClassifier

class sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=None)

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
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)

Initialize self. See help(type(self)) for accurate signature.

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

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.

partial_fit(X, y, classes=None, sample_weight=None)

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)

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)

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)

“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)

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