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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data FREE CHAPTER 2. Chapter 2: Encoding Categorical Variables 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Replacing categories with counts or the frequency of observations

In count with counts or frequency of observations” or frequency encoding, we replace the categories with the count or the fraction of observations showing that category. That is, if 10 out of 100 observations show the blue category for the Color variable, we would replace blue with 10 when doing count encoding, or with 0.1 if performing frequency encoding. These encoding methods are useful when there is a relationship between the category frequency and the target. For example, in sales, the frequency of a product may indicate its popularity.

Note

If two different categories are present in the same number of observations, they will be replaced by the same value, which may lead to information loss.

In this recipe, we will perform count and frequency encoding using pandas and feature-engine.

How to do it...

We’ll start by encoding one variable with pandas and then we’ll automate the process...

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