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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (15) Chapters Close

Preface 1. A Process for Success 2. Linear Regression – The Blocking and Tackling of Machine Learning FREE CHAPTER 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Classification methods and linear regression

So, why can't we just use the least squares regression method that we learned in the previous chapter for a qualitative outcome? Well, as it turns out, you can but at your own risk. Let's assume for a second that you have an outcome that you are trying to predict and it has three different classes: mild, moderate, and severe. You and your colleagues also assume that the difference between mild and moderate and moderate and severe is an equivalent measure and a linear relationship. You can create a dummy variable where zero is equal to mild, one is equal to moderate, and two is equal to severe. If you have reason to believe this, then linear regression might be an acceptable solution. However, qualitative assessments such as the previous ones might lend themselves to a high level of measurement error that can bias the OLS. In most business problems, there is no scientifically acceptable way to convert a qualitative response to one that...

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