What is this about?

It covers methodological aspects of the error in predictive models, how to measure it through cross-validation data and its similitude with bootstrapping technique. And how these strategies are used internally by some predictive models such us random forest or gradient boosting machines.

There is also a chapter about how to validate models when time is involved, which is similar to classical train/test validation.

Regarding predictive models with the multi-label outcome...

There is a chapter about gain and lift quality measures, which gives light to one important aspect when evaluation these models: to be sure the model correctly orders the cases according to their propensity to belong to the specific label.

Related to this point, it's recommended to read the Scoring Data chapter, as a complement to the validation techniques.

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