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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 polynomial expansion

Existing variables can be combined to create new insightful features. We discussed how to combine variables using common mathematical and statistical operations in the previous two recipes, Combining multiple features with statistical operations and Combining pairs of features with mathematical functions. A combination of one feature with itself, that is, a polynomial combination of the same feature, can also be quite informative or increase the predictive power of our algorithms. For example, in cases where the target follows a quadratic relationship with a variable, creating a second degree polynomial of the feature allows us to use it in a linear model, as shown in the following screenshot:

In the plot on the left, due to the quadratic relationship between the target, y, and the variable, x, there is a poor linear fit. Yet, in the...

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