Introduction
In this chapter, you will learn about cross-validation, a resampling technique that leads to a very accurate and robust estimation of a model's performance in comparison to the model evaluation approaches discussed in the previous chapters. This chapter starts with an in-depth discussion about why we need to use cross-validation for model evaluation, the underlying basics of cross-validation, its variations, and a comparison between them. Next, we will move on to implementing cross-validation on Keras deep learning models. We will also focus on how to use Keras wrappers with scikit-learn to allow Keras models to be treated as estimators in a scikit-learn workflow. You will then learn how to implement cross-validation in scikit-learn, and finally bring it all together and perform cross-validation using scikit-learn on Keras deep learning models. Lastly, you will learn about using cross-validation to perform more than just model evaluation. You will learn how a cross-validation...
