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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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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 (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 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

Other linear model considerations

Before moving on, there are two additional linear model topics that we need to discuss. The first is the inclusion of a qualitative feature, and the second is an interaction term; both are explained in the following sections.

Qualitative features

A qualitative feature, also referred to as a factor, can take on two or more levels such as Male/Female or Bad/Neutral/Good. If we have a feature with two levels, say gender, then we can create what is known as an indicator or dummy feature, arbitrarily assigning one level as 0 and the other as 1. If we create a model with just the indicator, our linear model would still follow the same formulation as before, that is, Y = B0 + B1x + e. If we code the feature as male being equal to 0 and...

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