class sklearn.multiclass.OneVsOneClassifier(estimator, n_jobs=None)
[source]
Onevsone 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 onevstherest, 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 onevstherest, the complete dataset is used n_classes
times.
Read more in the User Guide.
Parameters: 


Attributes: 

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])  Returns the mean accuracy on the given test data and labels. 
set_params (**params)  Set the parameters of this estimator. 
__init__(estimator, n_jobs=None)
[source]
decision_function(X)
[source]
Decision function for the OneVsOneClassifier.
The decision values for the samples are computed by adding the normalized sum of pairwise 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: 


Returns: 

fit(X, y)
[source]
Fit underlying estimators.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

partial_fit(X, y, classes=None)
[source]
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: 


Returns: 

predict(X)
[source]
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: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html