Class Weights

Implement class weights in SVM to bias the model towards under-represented classes.

In SVM classification, weights are a biasing mechanism for specifying the relative importance of target values (classes).

SVM models are automatically initialized to achieve the best average prediction across all classes. However, if the training data does not represent a realistic distribution, you can bias the model to compensate for class values that are under-represented. If you increase the weight for a class, then the percent of correct predictions for that class must increase.