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Feature
Engineering
for Machine Learning
PRINCIPLES AND TECHNIQUES FOR DATA SCIENTISTS
Alice Zheng & Amanda Casari
Principles and Techniques for Data Scientists
Feature Engineering for
Machine Learning
Alice Zheng and Amanda Casari
Beijing
Boston Farnham Sebastopol
Tokyo
Feature Engineering for Machine Learning
by Alice Zheng and Amanda Casari
Copyright © 2018 Alice Zheng, Amanda Casari. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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Editors:
Rachel Roumeliotis and Jeff Bleiel
Production Editor:
Kristen Brown
Copyeditor:
Rachel Head
Proofreader:
Sonia Saruba
April 2018:
First Edition
Indexer:
Ellen Troutman
Interior Designer:
David Futato
Cover Designer:
Karen Montgomery
Illustrator:
Rebecca Demarest
Revision History for the First Edition
2018-03-23:
First Release
See
http://oreilly.com/catalog/errata.csp?isbn=9781491953242
for release details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc.
Feature Engineering for Machine
Learning,
the cover image, and related trade dress are trademarks of O’Reilly Media, Inc.
While the publisher and the authors have used good faith efforts to ensure that the information and
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or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.
978-1-491-95324-2
[LSI]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1.
The Machine Learning Pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Data
Tasks
Models
Features
Model Evaluation
1
1
2
3
3
2.
Fancy Tricks with Simple Numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Scalars, Vectors, and Spaces
Dealing with Counts
Binarization
Quantization or Binning
Log Transformation
Log Transform in Action
Power Transforms: Generalization of the Log Transform
Feature Scaling or Normalization
Min-Max Scaling
Standardization (Variance Scaling)
ℓ
2
Normalization
Interaction Features
Feature Selection
Summary
Bibliography
Bag-of-X: Turning Natural Text into Flat Vectors
6
8
9
10
15
19
23
29
30
31
32
35
38
39
39
42
iii
3.
Text Data: Flattening, Filtering, and Chunking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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