As introduced in Chapter 1, Applied Machine Learning Quick Start, one of the preprocessing steps is focused on feature selection, also known as attribute selection. The goal is to select a subset of relevant attributes that will be used in a learned model. Why is feature selection important? A smaller set of attributes simplifies the models and makes them easier for users to interpret. This usually requires shorter training and reduces overfitting.
Attribute selection can take the class value into account or it cannot. In the first case, an attribute selection algorithm evaluates the different subsets of features and calculates a score that indicates the quality of selected attributes. We can use different searching algorithms, ...
