11. Dimensionality Reduction and Feature Selection

In this chapter, we explore the essential concepts of dimensionality reduction and feature selection. These techniques play a pivotal role in enhancing the efficiency and interpretability of various machine learning models by extracting the most relevant information from high-dimensional datasets. Through a comprehensive examination of methods such as Principal Components Analysis (PCA), Singular Value Decomposition (SVD), t-Distributed Stochastic Neighbor Embedding (t-SNE), Linear Discriminant Analysis (LDA), and feature selection strategies, readers will gain a deeper understanding of how to streamline their data and improve model performance. Practical insights and case studies further illustrate the application and benefits of dimensionality reduction and feature selection in real-world scenarios.

• 11.1. Introduction to Dimensionality Reduction
• 11.2. Principal Components Analysis (PCA)
• 11.3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
• 11.4. Linear and Quadratic Discriminant Analyses
• 11.5. Recursive Feature Elimination (RFE)
• 11.6. Practical Considerations in Dimensionality Reduction