Hold-Out Validation
To avoid over-fitting, an independent test set is preferred.
A natural approach is to split the available data
into two non-overlapped parts: one for training and
the other for testing. The test data is held out and not
looked at during training. Hold-out validation avoids
the overlap between training data and test data, yielding
a more accurate estimate for the generalization
performance of the algorithm.