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Feature Engineering Made Easy

You're reading from   Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Length 316 pages
Edition 1st Edition
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Authors (2):
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Divya Susarla Divya Susarla
Author Profile Icon Divya Susarla
Divya Susarla
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Feature Engineering FREE CHAPTER 2. Feature Understanding – What's in My Dataset? 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 9. Other Books You May Enjoy

Dimension reduction – feature transformations versus feature selection versus feature construction

In the last section, I mentioned how we could squish datasets to have fewer columns to describe data in new ways. This sounds similar to the concept of feature selection: removing columns from our original dataset to create a different, potentially better, views of our dataset by cutting out the noise and enhancing signal columns. While both feature selection and feature transformation are methods of performing dimension reduction, it is worth mentioning that they could not be more different in their methodologies. 

Feature selection processes are limited to only being able to select features from the original set of columns, while feature transformation algorithms use these original columns and combine them in useful ways to create new columns that are better at describing...

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