sklearn.multiclass.OutputCodeClassifier

class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=None, n_jobs=None)

(Error-Correcting) Output-Code multiclass strategy.

Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to project new points in the class space and the class closest to the points is chosen. The main advantage of these strategies is that the number of classifiers used can be controlled by the user, either for compressing the model (0 < code_size < 1) or for making the model more robust to errors (code_size > 1). See the documentation for more details.

Read more in the User Guide.

Parameters:

estimatorestimator object

An estimator object implementing fit and one of decision_function or predict_proba.

code_sizefloat, default=1.5

Percentage of the number of classes to be used to create the code book. A number between 0 and 1 will require fewer classifiers than one-vs-the-rest. A number greater than 1 will require more classifiers than one-vs-the-rest.

random_stateint, RandomState instance, default=None

The generator used to initialize the codebook. Pass an int for reproducible output across multiple function calls. See Glossary.

n_jobsint, default=None

The number of jobs to use for the computation: the multiclass problems are computed 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 int(n_classes * code_size) estimators

Estimators used for predictions.

classes_ndarray of shape (n_classes,)

Array containing labels.

code_book_ndarray of shape (n_classes, code_size)

Binary array containing the code of each class.

n_features_in_int

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

New in version 1.0.

See also

OneVsRestClassifier

One-vs-all multiclass strategy.

OneVsOneClassifier

One-vs-one multiclass strategy.

References

[1]

“Solving multiclass learning problems via error-correcting output codes”, Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995.

[2]

“The error coding method and PICTs”, James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998.

[3]

“The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) 2008.

Examples

>>> from sklearn.multiclass import OutputCodeClassifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> clf = OutputCodeClassifier(
...     estimator=RandomForestClassifier(random_state=0),
...     random_state=0).fit(X, y)
>>> clf.predict([[0, 0, 0, 0]])
array([1])

Methods

fit(X, y)	Fit underlying estimators.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict multi-class targets using underlying estimators.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

fit(X, y)

Fit underlying estimators.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Data.

yarray-like of shape (n_samples,)

Multi-class targets.

Returns:

selfobject

Returns a fitted instance of self.

get_params(deep=True)

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.

predict(X)

Predict multi-class targets using underlying estimators.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Data.

Returns:

yndarray of shape (n_samples,)

Predicted multi-class targets.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

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

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)

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.