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sklearn.multiclass.OneVsOneClassifier

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

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 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.

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 this estimator.
predict(X) Predict multi-class targets using underlying estimators.
score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
__init__(estimator, n_jobs=1)
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 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.

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, sample_weight=None)

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,)

True labels for X.

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

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. 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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :
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