sklearn.multiclass.OneVsOneClassifier

class sklearn.multiclass.OneVsOneClassifier(estimator, *, n_jobs=None)

One-vs-one multiclass strategy.

This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_classes * (n_classes - 1) / 2 classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don’t scale well with n_samples. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used n_classes times.

Read more in the User Guide.

Parameters:

estimatorestimator object

A regressor or a classifier that implements fit. When a classifier is passed, decision_function will be used in priority and it will fallback to predict_proba if it is not available. When a regressor is passed, predict is used.

n_jobsint, default=None

The number of jobs to use for the computation: the n_classes * ( n_classes - 1) / 2 OVO 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 n_classes * (n_classes - 1) / 2 estimators

Estimators used for predictions.

classes_numpy array of shape [n_classes]

Array containing labels.

n_classes_int

Number of classes.

pairwise_indices_list, length = len(estimators_), or None

Indices of samples used when training the estimators. None when estimator’s pairwise tag is False.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

OneVsRestClassifier

One-vs-all multiclass strategy.

OutputCodeClassifier

(Error-Correcting) Output-Code multiclass strategy.

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.multiclass import OneVsOneClassifier
>>> from sklearn.svm import LinearSVC
>>> X, y = load_iris(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, shuffle=True, random_state=0)
>>> clf = OneVsOneClassifier(
...     LinearSVC(random_state=0)).fit(X_train, y_train)
>>> clf.predict(X_test[:10])
array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1])

Methods

decision_function(X)	Decision function for the OneVsOneClassifier.
fit(X, y)	Fit underlying estimators.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, classes])	Partially fit underlying estimators.
predict(X)	Estimate the best class label for each sample in X.
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.

decision_function(X)

Decision function for the OneVsOneClassifier.

The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading to a tie.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

Returns:

Yarray-like of shape (n_samples, n_classes) or (n_samples,)

Result of calling decision_function on the final estimator.

Changed in version 0.19: output shape changed to (n_samples,) to conform to scikit-learn conventions for binary classification.

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

The fitted underlying estimator.

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.

property n_classes_

Number of classes.

partial_fit(X, y, classes=None)

Partially fit underlying estimators.

Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables.

Parameters:

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

Data.

yarray-like of shape (n_samples,)

Multi-class targets.

classesarray, shape (n_classes, )

Classes across all calls to partial_fit. Can be obtained via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls.

Returns:

selfobject

The partially fitted underlying estimator.

predict(X)

Estimate the best class label for each sample in X.

This is implemented as argmax(decision_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair.

Parameters:

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

Data.

Returns:

ynumpy array 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.