# How to analysis this ROC curve and improve the performance? [closed]

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!

## closed as too broad by gung♦Apr 19 '18 at 18:29

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• How to improve the performance is model specific. ROC only gives you feedback on your model. – SmallChess Feb 24 '17 at 1:25
• @StudentT, thanks for comments! Is this a valid ROC curve, along with the confusion matrix? From the confusion matrix, the Class 2 are all mis-classified. – mining Feb 24 '17 at 1:31