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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

Scaling with the median and quantiles

When scaling variables to the median and quantiles, the median value is removed from the observations and the result is divided by the inter-quartile range (IQR). The IQR is the range between the 1st quartile and the 3rd quartile, or, in other words, the range between the 25th quantile and the 75th quantile:

This method is known as robust scaling because it produces more robust estimates for the center and value range of the variable, and is recommended if the data contains outliers. In this recipe, we will implement scaling with the median and IQR by utilizing scikit-learn.

How to do it...

To begin, we will import the required packages, load the dataset, and prepare the...

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