Automatic feature engineering
Feature engineering is the second step of a typical machine learning pipeline (see Figure 13.1). It consists of three major steps: feature selection, feature construction, and feature mapping. Let’s look at each of them in turn:
Feature selection aims at selecting a subset of meaningful features by discarding those that are making little contribution to the learning task. In this context, “meaningful” truly depends on the application and the domain of your specific problem.
Feature construction has the goal of building new derived features, starting from the basic ones. Frequently, this technique is used to allow better generalization and to have a richer representation of the data.
Feature mapping aims at altering the original feature space by means of a mapping function. This can be implemented in multiple ways; for instance, it can use autoencoders (see Chapter 8), PCA (see Chapter 7), or clustering (see Chapter 7)...
