Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)

Performing discretization followed by categorical encoding

After discretization, the intervals of the variable can be treated as a discrete numerical variable, or as categories in a categorical variable. If treated as categorical, we can follow up the discretization by reordering the intervals according to the target value, as we did in the Encoding with integers in an ordered manner recipe in Chapter 3, Encoding Categorical Variables, to create a monotonic relationship between the intervals and the target. In this recipe, we will combine these two feature engineering techniques using Feature-engine and the Boston House Prices dataset from scikit-learn.

How to do it...

To perform equal-frequency discretization...