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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 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 only. One-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...
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