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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

Feature Transformations

So far, in this text, we have encountered feature engineering tools from what seems like all possible angles of data. From analyzing tabular data in order to ascertain levels of data to constructing and selecting columns using statistical measures in order to optimize our machine learning pipelines, we have been on a remarkable journey of dealing with features in our data.

It is worth mentioning once more that enhancements of machine learning come in many forms. We generally consider our two main metrics as accuracy and prediction/fit times. This means that if we can utilize feature engineering tools to make our pipeline have higher accuracy in a cross-validated setting, or be able to fit and/or predict data quicker, then we may consider that a success. Of course, our ultimate hope is to optimize for both accuracy and time, giving us a much better pipeline...

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