svm one vs rest

SVM one vs rest (OvR) is a binary classification technique that involves training multiple binary classifiers on a multi-class dataset. In this approach, a single classifier is trained for each class against all other classes.

For example, if there are three classes (A, B, and C), then three binary classifiers are trained: A vs (B + C), B vs (A + C), and C vs (A + B). Once the classifiers are trained, the class with the highest confidence score is predicted as the output.

SVM OvR is a popular approach because it is simple and easy to implement. It also allows for the use of binary classifiers that are more efficient and easier to optimize than multi-class classifiers. However, it may not always produce the best results, especially when the classes are highly imbalanced or overlapping.

Overall, SVM OvR is a useful technique for tackling multi-class classification problems that require binary classifiers.