Applied Supervised Learning with R

Chapter 6:

Feature Selection and Dimensionality Reduction

Learning Objectives

By the end of this chapter, you will be able to:

• Implement feature engineering techniques such as discretization, one-hot encoding, and transformation
• Execute feature selection methods on a real-world dataset using univariate feature selection, correlation matrix, and model-based feature importance ranking
• Apply feature reduction using principal component analysis (PCA) for dimensionality reduction, variable reduction with clustering, and linear discriminant analysis (LDA)
• Implement PCA and LDA and observe the differences between them

In this chapter, we will explore the feature selection and dimensionality reduction methods to build an effective feature set ...