There are 3 classes with imbalanced number of training samples. I've got the following classification metrics:
and the following ROC curve on the validation set:
As shown in the confusion matrix on validation set, it seems all the samples of
Class 2 are wrongly classified. But from the ROC curve, it seems to some extent the
Class 2 are good classified with a reasonable threshold.
My question is, how to improve the classification performance on
Class 2? Any comments are appreciated. Thanks!