Summary
In this chapter we analyzed two important steps in the machine learning pipeline:
- Feature selection
- Feature engineering
As we saw, these two processes currently are as much an art as they are a science. Picking a model to use in the pipeline potentially is an easier task than deciding which features to drop and which features to generate to add to the model. This chapter is not meant to be a comprehensive analysis of feature selection and feature engineering, but rather it's a small taste and hopefully it whets your appetite to explore this topic further.
In the next chapter, we'll start getting into the meat of machine learning. We will be building machine learning models starting with supervised learning models.
