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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 Variable Discretization

Discretization, or binning, is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals, also called bins, that span the range of the variable values. Discretization is used to change the distribution of skewed variables and to minimize the influence of outliers, and hence improve the performance of some machine learning models.

How does discretization minimize the effect of outliers? Discretization places outliers into the lower or higher intervals, together with the remaining inlier values of the distribution. Hence, these outlier observations no longer differ from the rest of the values at the tails of the distribution, as they are now all together in the same interval or bin. Also, if sorting observations across bins with equal frequency, discretization spreads the values of a...

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