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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

Encoding with integers in an ordered manner

In the Replacing categories with ordinal numbers recipe, we replaced categories with integers, which were assigned arbitrarily. This encoding works well with non-linear machine learning algorithms that can navigate through the arbitrarily assigned digits to try and find patterns that relate them to the target. However, this encoding method may not work so well with linear models.

We can instead assign integers to the categories given the target values. To do this, we do the following:

  1. Calculate the mean value of the target per category.
  2. Order the categories from the one with the lowest to the one with the highest target mean value.
  3. Assign digits to the ordered categories, starting with 0 to the first category all of the way up to k-1 to the last category, where k is the number of distinct categories.

This encoding technique creates...

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