Feature Selection and Dimensionality Reduction: Investigating feature selection and dimensionality reduction techniques to improve model performance and computational efficiency

Abstract

Feature selection and dimensionality reduction are crucial steps in machine learning model development, aiming to improve performance and reduce computational complexity. This paper provides a comprehensive overview of various techniques in these domains, analyzing their impact on model efficiency and effectiveness. We explore methods such as filter, wrapper, and embedded approaches for feature selection, along with principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders for dimensionality reduction. Through empirical evaluations and case studies, we highlight the strengths and limitations of each technique, providing insights into their practical applications and best practices. This paper serves as a guide for practitioners and researchers seeking to enhance their understanding and utilization of feature selection and dimensionality reduction methods in machine learning.