Learn over 20 feature selection methods using Python, scikit-learn, and Feature-engine. Apply filter, wrapper, embedded, and recursive techniques to select predictive features and build efficient machine learning models
Key Features
- Master over 20 feature selection techniques using practical Python examples
- Apply filter, wrapper, embedded, and recursive feature selection methods
- Remove redundant variables to build faster, simpler, and more interpretable ML models
Book Description
Feature selection is essential for building efficient machine learning models, yet choosing the right variables can be challenging. Irrelevant, redundant, and highly correlated features increase complexity, slow training, and can affect model performance and interpretability Feature Selection in Machine Learning provides a practical guide to identifying the most predictive variables using Python. Starting with the fundamentals, you'll learn why feature selection matters and how different methods help simplify datasets without losing valuable information You'll begin by removing constant, quasi-constant, duplicate, and correlated features before exploring statistical techniques such as chi-square, ANOVA, and mutual information. The book then introduces univariate selection, wrapper methods, embedded techniques using Lasso and decision trees, and recursive feature elimination. You'll also explore alternative approaches, including feature permutation, minimum redundancy maximum relevance (MRMR), and Boruta With practical code examples and open-source Python libraries, you'll learn to compare techniques and select the right approach for your data and models. By the end, you'll be equipped to build simpler, faster, and more interpretable machine learning solutions
What you will learn
- Understand feature selection and its impact on ML performance
- Identify and remove constant, duplicate, and correlated features
- Apply statistical methods such as chi-square, ANOVA, and mutual information
- Select predictive variables using univariate techniques
- Implement forward, backward, and exhaustive feature selection
- Use Lasso and decision trees for embedded feature selection
- Apply recursive feature elimination and feature permutation
- Explore advanced methods including MRMR and Boruta
Who this book is for
This book is for data scientists, machine learning engineers, data analysts, and Python developers who want to improve model performance through effective feature selection. It is particularly useful for practitioners working with high-dimensional datasets or looking to simplify models without sacrificing predictive power. Readers should have basic Python programming skills, familiarity with pandas and scikit-learn, and a foundational understanding of machine learning concepts
