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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

Constructing a decision tree using Rpart

While OneR is very good at determining simple classification rules, it is not able to construct full decision trees. However, we can extract a sample from Spark and route it to any R decision tree algorithm, such as rpart.

First collect the sample

To illustrate this, let's first take a 50% sample of the stop and frisk dataframe. We also want to make sure that the amount of data we extract can be processed easily by base R, which has a memory limitation that is dependent upon the CPU size.

  • The code below will first extract a 50% sample from Spark and store it in a local R dataframe named dflocal.
  • Then it will run an str() command to verify the rowcount and the metadata:
dflocal...
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