better option is to split your data into two sets: the training set and the test set. As these names imply, you train your model using the training set, and you test it using the test set. The error rate on new cases is called the generalization error (or out-of-sample error), and by evaluating your model on the test set, you get an estimation of this error. This value tells you how well your model will perform on instances it has never seen before. If the training error is low (i.e., your model makes few mistakes on the training set) but the generalization error is high, it means that your
...more
