8.19.3. sklearn.multiclass.OneVsOneClassifier

class sklearn.multiclass.OneVsOneClassifier(estimator)

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.

Parameters :

estimator : estimator object

An estimator object implementing fit and predict.

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.

Methods

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

__init__(estimator)

fit(X, y)

Fit underlying estimators.

Parameters :

X: {array-like, sparse matrix}, shape = [n_samples, n_features] :

Data.

y : numpy array of shape [n_samples]

Multi-class targets.

Returns :

self :

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Predict multi-class targets using underlying estimators.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Data.

Returns :

y : numpy array of shape [n_samples]

Predicted multi-class targets.

score(X, y)

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

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :