Train and validation data structure
If the train data and the validation data are the same, the trained classifier will have a high accuracy, because it has already seen the data. That is why we use train-test splits. We take 60-70% of the training data to train the classifier, and then run the classifier against 30-40% of the data, the validation data which the classifier has not seen yet. This helps measure the accuracy of the classifier and its behavior, such as over fitting or under fitting, against a real test set with no labels.
What will happen if I use the same training data and validation data for my machine learning classifier?