# Which classification model should I choose and Why?

I am working on a research-based assignment where I suppose to build a 3-class (bad, medium, good) classification using SVM. The dataset provided is imbalanced. The train:test splitting ratio is 75:25 with stratified method.

First Model - I did not oversample the data

Second Model - I oversample minority class using RandomOverSampler() in the the train set

Third Model - I oversample minority class using RandomOverSampler() in the original dataset, then only i split into train and test set.

Based on all 3 models' result, which model should be chosen (even if there is room for improvement for both 3 models) in terms of logicalness, correctness and also why?

• I think there might be room for improvement, but your third model is overfit and you should not consider it. Feb 12 '19 at 14:03
• @user2974951 Thanks for you reply. Yes, of course improvement is needed. But at this stage, which model should be consider as the best one and why since you stated not to consider third model? Feb 12 '19 at 14:07