Data Science, 2nd Edition

Chapter 14

Feature Selection

Abstract

This chapter introduces a preprocessing step that is critical for a successful data science exercise: feature selection. Feature selection is known by several alternative terms such as attribute weighting, dimension reduction, and so on. There are two main styles of feature selection: filtering the key attributes before modeling (filter style) or selecting the attributes during the process of modeling (wrapper style). A few filter-based methods will be discussed such as principal component analysis, information gain, and chi-square, as well as a couple of wrapper-type methods like forward selection and backward elimination.

Keywords

Attribute weighting; backward elimination; chi-square test; dimension reduction; ...