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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 2. Feature Understanding – What's in My Dataset? FREE CHAPTER 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

The four levels of data

We already know that we can identify data as being either qualitative or quantitative. But, from there, we can go further. The four levels of data are:

  • The nominal level
  • The ordinal level
  • The interval level
  • The ratio level

Each level comes with a varying level of control and mathematical possibilities. It is crucial to know which level data lives on because it will dictate the types of visualizations and operations you are allowed to perform.

The nominal level

The first level of data, the nominal level, has the weakest structure. It consists of data that are purely described by name. Basic examples include blood type (A, O, AB), species of animal, or names of people. These types of data are all qualitative...

You have been reading a chapter from
Feature Engineering Made Easy
Published in: Jan 2018
Publisher: Packt
ISBN-13: 9781787287600
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