The core idea of Binary Relevance is to decompose the multi-label classification problem and transform it into q binary classification problems, where each binary classifier corresponds to a label to be predicted.

Advantage: The realization method is simple and easy to understand; When there is no mutual dependence between the y values, the model works well

Shortcoming: If there is a mutual dependence between y, then the generalization ability of the final model is relatively weak; It is necessary to construct q two classifiers, where q is the number of y values to be predicted. When q is large, there are correspondingly more models to be constructed.