Fundamentals of Data Science

8: Feature selection

Abstract

In Data Science and machine learning, feature selection and extraction are key procedures that are essential for improving model performance, lowering computational complexity, and drawing conclusions from data. The main ideas, techniques, and procedures used in feature selection and extraction are discussed in this chapter. It offers an in-depth discussion of these methods and how they might help with predictive modeling and data analysis. The chapter opens by defining the differences between feature selection and feature extraction while emphasizing the special advantages and use cases of each. It delves into the crucial aspects of feature selection and walks readers through a methodical process for choosing the ...