class sklearn.multiclass.OneVsOneClassifier(estimator, n_jobs=1)[source]

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.


estimator : estimator object

An estimator object implementing fit and one of decision_function or 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.


estimators_ : list of n_classes * (n_classes - 1) / 2 estimators

Estimators used for predictions.

classes_ : numpy array of shape [n_classes]

Array containing labels.


decision_function(X) Decision function for the OneVsOneClassifier.
fit(X, y) Fit underlying estimators.
predict(X) Estimate the best class label for each sample in X.
__init__(estimator, n_jobs=1)[source]

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:X : array-like, shape = [n_samples, n_features]
Returns:Y : array-like, shape = [n_samples, n_classes]
fit(X, y)[source]

Fit underlying estimators.


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


y : array-like, shape = [n_samples]

Multi-class targets.


self :


Get parameters for this estimator.


deep: boolean, optional :

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


params : mapping of string to any

Parameter names mapped to their values.


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.


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



y : numpy array of shape [n_samples]

Predicted multi-class targets.

score(X, y, sample_weight=None)[source]

Returns 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.


X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.


score : float

Mean accuracy of self.predict(X) wrt. y.


Set the parameters of this 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 :