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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 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 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

Modeling and evaluation


With the data prepared, we will begin the modeling process. For comparison purposes, we will create a model using best subsets regression like the previous two chapters and then utilize the regularization techniques.

Best subsets

The following code is, for the most part, a rehash of what we developed in Chapter 2, Linear Regression - The Blocking and Tackling of Machine Learning. We will create the best subset object using the regsubsets() command and specify the train portion of data. The variables that are selected will then be used in a model on the test set, which we will evaluate with a mean squared error calculation.

The model that we are building is written out as lpsa ~ . with the tilde and period stating that we want to use all the remaining variables in our data frame, with the exception of the response:

> subfit <- regsubsets(lpsa ~ ., data = train)

With the model built, you can produce the best subset with two lines of code. The first one turns the summary...

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