Chapter 6. Feature Selection and Dimensionality Reduction

Note

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

Note

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