So when you read about cross validation, they mention that you split the data in k-1 folds and then test it against one partion of the data. I am currently doing cross validation on my decision trees using the whole data and then test in on the same data. That is wrong if I am not mistaken? Because I want to assess the precision, recall and accuracy from the confusionMatrix/tables I can get once predict my model on the test data.
Is the correct way to divide your data in training (~80%) (cross validation set) and test set (~20%) to later validate your model? This division result in: 60 % training 20 % validation 20 % testing.