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 one-hot encoding of frequent categories

One-hot encoding represents each category of a categorical variable with a binary variable. Hence, one-hot encoding of highly cardinal variables or datasets with multiple categorical features can expand the feature space dramatically. To reduce the number of binary variables, we can perform one-hot encoding of the most frequent categories onlyOne-hot encoding of top categories is equivalent to treating the remaining, less frequent categories as a single, unique category, which we will discuss in the Grouping rare or infrequent categories recipe toward the end of this chapter.

For more details on variable cardinality and frequency, visit the Determining cardinality in categorical variables recipe and the Pinpointing rare categories in categorical variables recipe in Chapter 1, Foreseeing Variable Problems...