Learning Data Mining with Python - Second Edition

Running the algorithm

The previous results are quite good, based on our testing set of data, based on the testing set. However, what happens if we get lucky and choose an easy testing set? Alternatively, what if it was particularly troublesome? We can discard a good model due to poor results resulting from such an unlucky split of our data.

The cross-fold validation framework is a way to address the problem of choosing a single testing set and is a standard best-practice methodology in data mining. The process works by doing many experiments with different training and testing splits, but using each sample in a testing set only once. The procedure is as follows:

1. Split the entire dataset into several sections called folds.
2. For each fold in ...