To estimate the generalization error, we split our data into two parts: training data and testing data. A general rule of thumb is to split them by the training: testing ratio, that is, 70:30. We first train the predictor on the training data, then predict the values for the test data, and finally, compute the error, that is, the difference between the predicted and the true values. This gives us an estimate of the true generalization error.

The estimation is based on the two following assumptions: first, we assume that the test set is an unbiased sample from our dataset; and second, we assume that the actual new data will reassemble the distribution as our training and testing examples. The first assumption can be mitigated by cross-validation and stratification. Also, if it is scarce, one can't afford to leave out a considerable amount of data for a separate test set, as learning algorithms do not perform well if they don't receive enough data. In such cases, cross-validation is used instead.