Feature Selection in Machine Learning

Book Description

Feature selection is one of the most impactful steps in any machine learning pipeline, yet it's often overlooked or done poorly.

When you include irrelevant or redundant features in your model, you pay for it: slower training, higher complexity, and models that overfit and fail in production. Feature selection fixes that. By identifying and keeping only the most predictive variables, you build models that are faster, simpler, and more reliable; and whose outputs are actually interpretable by the people who use them.

This book gives you a complete, practical toolkit of over 20 feature selection methods, covering everything from foundational techniques to advanced approaches used in data science competitions and industry.

You'll work through filter, wrapper, and embedded methods, then go further, exploring methods designed by computer science researchers and battle-tested practitioners that are faster or more scalable for real-world data.

Specifically, you'll learn how to:

• Remove useless and redundant features by examining variability and correlation.
• Choose features based on statistical tests such as ANOVA, chi-square, and mutual information.
• Select features using Lasso regularization or decision tree feature importance; methods baked directly into the machine learning process.
• Apply recursive feature elimination, addition, and value permutation to squeeze the best performance from your models.

Each chapter follows a clear structure: first the fundamentals, then a hands-on Python implementation using Scikit-learn, MLXtend, and Feature-engine. You'll be able to select the best numerical and categorical features for regression and classification in just a few lines of code.

The book also comes with a GitHub repository containing the full source code, ready for you to download, adapt, and use in your own projects.

One important distinction worth knowing: feature selection differs from dimensionality reduction in that it doesn't transform your variables; it simply selects the most valuable ones, keeping your data fully interpretable.

By the end, you'll know what feature selection is and which method to reach for, and why.