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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Length 372 pages
Edition 1st 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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Table of Contents (13) Chapters Close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data FREE CHAPTER 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. Other Books You May Enjoy

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...

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