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 binary encoding

Binary encoding is an alternative categorical encoding technique that uses binary code, that is, a sequence of zeroes and ones, to represent the different categories of the variable. How does it work? First, the categories are arbitrarily replaced by ordinal numbers, as shown in the intermediate step of the following table. Then, those numbers are converted into binary code. For example, the integer 1 can be represented as the sequence 01, the integer 2 as 10, the integer 3 as 00, and 4 as 11. The digits in the two positions of the binary string become the columns, which are the encoded representation of the original variable:

Color Intermediate step 1st 2nd
Blue 1 0 1
Red 2 1 0
Green 3 0 0
Yellow 4 1 1

 

Binary encoding encodes the data in fewer dimensions than one-hot encoding. In our example, the color variable would be encoded...