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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from  Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Pages 420 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (23) Chapters close

Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning 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 and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Classification methods and linear regression


So, why can't we just use the least square 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 0 is equal to mild, 1 is equal to moderate, and 2 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 is quantitative...

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