I am a little confused about this situation: I have a dataset for classification problem. It is divided into a training and a testing set. And I've used the training set to generate an artificial dataset from it (using an oversampling technique) to get a more balanced dataset (because the ratio between the two classes was very y unbalanced). I want to test my artificial dataset. So I want to use some classifiers such as Decision Tree, to compare with the orginal datasets.
The thing is that I am training the classifier with the artificial data, and then using that model I provide the testing dataset to get the prediction (as a final step: I get a confusion matrix); and also using the training dataset to predict and get another confusion matrix but in this case, as validation values. Is this approach OK? I am doing it well? should I training the model using the training dataset or the artificial?
Sorry if this is a little dum question, but I am new in this, and also I've seen only training and testing steps, and not the validation of models...