Chapter 18
Performing Cross-Validation, Selection, and Optimization
IN THIS CHAPTER
Learning about overfitting and underfitting
Choosing the right metric to monitor
Cross-validating the results
Selecting the best features for your model
Optimizing hyperparameters
This chapter is about how machine learning algorithms learn, and it explores some methods for making them learn better. Machine learning algorithms can indeed learn from data. For instance, the four algorithms presented in the previous chapter, although not complex, can effectively estimate a class or a value after being presented with examples associated with outcomes. It is all a matter of learning by induction, which is the process of extracting general rules from specific examples. From childhood, humans commonly learn by seeing examples, deriving some general rules or ideas from them, and then successfully applying the derived rule to new situations as we grow up. For example, if we see someone being burned after touching ...
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