Feature Selection in Machine Learning
20+ feature selection methods to build simpler, faster, and more reliable ML models
$53.10
- Paperback
216 pages
- Release Date
30 October 2026
Summary
Feature Selection in Machine Learning
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.
This book provides a practical guide to identifying the most predictive variables using Python. Starting with the fundamentals, you’ll learn why feature selection m…
Book Details
| ISBN-13: | 9781808811111 |
|---|---|
| ISBN-10: | 1808811119 |
| Author: | Soledad Galli, PhD |
| Publisher: | Packt Publishing Limited |
| Imprint: | Packt Publishing Limited |
| Format: | Paperback |
| Number of Pages: | 216 |
| Release Date: | 30 October 2026 |
| Dimensions: | 216mm x 279mm |
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Soledad Galli, PhD
Soledad Galli is a data scientist, Python developer, developer advocate, instructor, and author with over 10 years of experience helping practitioners build and adopt machine-learning solutions with Python.
She is the creator and maintainer of Feature-engine, an open-source Python library for feature engineering and selection, which is downloaded more than 300,000 times each month and supported by a global community of contributors. Through her software, teaching, writing, and developer advocacy, she helps data scientists work more effectively and adopt practical tools faster.
Soledad is the author of the bestselling Python Feature Engineering Cookbook (now in its third edition) and Feature Selection in Machine Learning (second edition). She founded Train in Data, a practitioner-focused school, and teaches thousands of students through various platforms.
She has built and deployed machine-learning models for credit-risk assessment, insurance-claim routing, and fraud prevention. Her work has earned a Data Science Leaders’ Award and recognition as one of LinkedIn’s Top Voices in Data Science and Analytics.
Soledad holds an MSc in Biology and a PhD in Biochemistry.
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