In this subsection, we will describe an example of performing multiclass classification using the OVTR algorithm by converting the problem into equivalent multiple binary classification problems. The OVTR strategy breaks down the problem and trains each binary classifier per class. In other words, the OVTR classifier strategy consists of fitting one binary classifier per class. It then treats all the samples of the current class as positive samples, and consequently other samples of other classifiers are treated as negatives samples.
This is a modular machine learning technique no doubt. However, on the downside, this strategy requires a base classifier from the multiclass family. The reason is ...
