There are cases when the number of features available is so large that it adversely affects the overall solution. Not only is the processing and handling of a dataset with a huge number of attributes an issue but it also leads to difficulty in interpretation, visualization, and many more. These issues are formally termed as the curse of dimensionality.

Feature selection thus helps us identify representative sets of features that can be utilized in the modeling step without much loss of information. There are different techniques to perform feature selection; some of them are discussed in the later sections of the chapter.