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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Classification methods


If the response variable is qualitative/categorical (takes on categorical values such as gender, loan default, marital status, and such), then the problem can be called a classification problem regardless of the explanatory variables' type. There are various types of classification methods, but we will focus on logistic regression and Support Vector Machines in this section.

Following are a few examples of some implications of classification methods:

  • A customer buys a product or does not buy it

  • A person is diabetic or not diabetic

  • An individual applying for a loan would default or not

  • An e-mail receiver would read the e-mail or not

Logistic regression

Logistic regression measures the relation between the explanatory variables and the categorical response variable. We do not use linear regression for the categorical response variable because the response variable is not on a continuous scale and hence the error terms are not normally distributed.

So logistic regression is a...

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