A new, comprehensive book on Feature Selection with Python

Feature selection plays a pivotal role in data science. Otherwise, very well-performing models can be seriously affected by poor feature selection. However, this area has not received proper attention in current books in the market. In comparison, “Feature Selection in Machine Learning with Python” by Soledad Galli shines.

If you search for books on feature selection on Amazon, you will be surprised by how few you will find, and even fewer published in recent years. There are currently only two major books: this one by Soledad Galli and “Feature Engineering and Selection” by Max Kuhn and Kjell Johnson. Both are superior books that excel in different aspects. Several books on feature engineering include feature selection, but usually only in one chapter, if anything. These two books offer much better coverage.

Kuhn and Johnson’s book is comprehensive, clear, authoritative, and solid. It also covers feature engineering. I highly recommend it (see the link below to purchase it at Amazon). But it uses R instead of Python (which has a much broader appeal and usage, being one of the most popular and easy-to-learn languages). Feature selection doesn’t receive the same extensive coverage that Galli’s book does, which uses Python in a comprehensive way. The choice of programming language plays a crucial role as it restricts which libraries, methods, and environments you use for feature selection. Data Science requires excellent software engineering skills.

“Feature Selection in Machine Learning with Python” by Soledad Galli is clear, enjoyable, concise, and solid. This book covers several methods of feature selection, including filter, wrapper, and embedded methods. It also covers univariate feature selection, recursive feature addition, recursive feature elimination, and feature shuffling. It covers the best feature selection methods as well correlation of predictors — all with the latest libraries available in Python and popular datasets. The book layout, including its Python code, is pleasant in electronic formats (PDF, ePub, Kindle). Some books use a black or grey background for the code, which makes it hard to read on e-ink devices, but this book does not make this mistake.

This book fills an elusive gap. Its focus on feature selection with Python is unique. Galli’s style is clear and engaging. Do not miss it (link to purchase it at Amazon below).

To buy “Feature Engineering and Selection” by Max Kuhn and Kjell Johnson at Amazon in the US: https://amzn.to/3EZm3Ry
at Amazon in Germany: https://amzn.to/3PWY8Z7

To buy “Feature Selection in Machine Learning with Python” by Soledad Galli
at Amazon in the US: https://amzn.to/46qaqhS
at Amazon in Germany: https://amzn.to/3ZApuY2