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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Variable selection


The model we just worked with had a limited number of variables, so mechanical variable selection methods when dealing with a large number of variables were not really that pertinent. We were able to pinpoint the important ones via the regression model. However, for a model with a large number of variables we could use the glmulti package for the purpose of performing variable selection.

For the churn example that was generated, we have a small number of variables, so it is easy to demonstrate a variable selection and not so time consuming.

In the following code, we will set the maximum number of terms to include in the best regression to 10 in order to limit the computational time needed to perform an exhaustive search. We will also use the genetic algorithm option (method = "g") which can be much faster with larger datasets, since it only considers the best subsets of all of the combinations.

If you wish to perform an exhaustive search, use method = "h". However, be forewarned...

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