You can use One Vs. All
classification. In One Vs. All classification, you transform the multiclass classification task to multiple binary classification problems.
So for example, if you have 3 classes, you would turn the problem into 3 separate binary classification tasks.
For every individual class i
, you build a classifier where data points belonging to class i
are the positive samples, and all the rest of the data points are the negative samples.
To predict the output for a new test data point x
, you apply all the trained classifiers to point x
, and predict the label for which the corresponding
classifier outputs the highest confidence score