Summary

In this chapter, you learned about cross-validation, which is one of the most important resampling methods. It results in the best estimation of model performance on independent data. You learned about the basics of cross-validation and its two different variations, along with a comparison of them. You also learned about the Keras wrapper with scikit-learn, which is a very helpful tool that allows scikit-learn methods and functions such as performing cross-validation to be easily applied to Keras models. You learned the step-by-step process of implementing cross-validation in order to evaluate Keras deep learning models using the Keras wrapper with scikit-learn. Lastly, you learned that cross-validation estimations of model performance can be used to decide among different models for a particular problem or to decide about parameters (or hyperparameters) for a particular model. You practiced using cross-validation for this purpose by writing user-defined functions in order to perform...