ovo

One-vs-one strategy for reducing the problem of multiclass classification to multiple binary classification problems. This approach trains K (K − 1) / 2 binary classifiers for a K-way multiclass problem; each receives the samples of a pair of classes from the original training set, and must learn to distinguish these two classes. At prediction time, a voting scheme is applied: all K (K − 1) / 2 classifiers are applied to an unseen sample and the class that got the highest number of positive predictions gets predicted by the combined classifier. Like One-vs-rest, one-vs-one suffers from ambiguities in that some regions of its input space may receive the same number of votes.