Programming Machine Learning

Chapter 17Defeating Overfitting

Overfitting has been our nemesis throughout the book. In this chapter, we’ll finally confront this archenemy.

To refresh your memory, a system that overfits is like a student who learns by rote memory. She might be good at solving familiar problems from textbooks, but she’ll struggle when confronted with new problems. Likewise, an overfitting system could be okay at classifying its training data, and then fail when classifying data it hasn’t seen before.

In earlier chapters, you learned a strategy to work around overfitting: split your data into training, validation, and test sets. Use the training set to train the system, the validation set to tune its performance, and the test set for a final check-up. That ...