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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd 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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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Implementing maximum absolute scaling

Maximum absolute scaling scales the data to its maximum value – that is, it divides every observation by the maximum value of the variable:

<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mrow><msub><mi>x</mi><mrow><mi>s</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>e</mi><mi>d</mi></mrow></msub><mo>=</mo><mfrac><mi>x</mi><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mfrac></mrow></mrow></math>

As a result, the maximum value of each feature will be 1.0. Note that maximum absolute scaling does not center the data, and hence, it’s suitable for scaling sparse data. In this recipe, we will implement maximum absolute scaling with scikit-learn.

Note

Scikit-learn recommends using this transformer on data that is centered at 0 or on sparse data.

Getting ready

Maximum absolute scaling was specifically designed to scale sparse data. Thus, we will use a bag-of-words dataset that contains sparse variables for the recipe. In this dataset, the variables are words, the observations are documents, and the values are the number of times each word appears in the document. Most entries in the data are 0.

We will use a dataset consisting of a bag of words, which is available in the...

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