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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

Running the OneR model

The syntax to the OneR model should be familiar. The outcome variable frisked is specified on the left side of the formula (~) and the features are specified on the right side. As you will recall, the metacharacter (.) designates that all features will be used as predictors:

model <- OneR(train_data, frisked ~ ., verbose = TRUE) 
summary(model)

The (partial) summary output displays the accuracy based upon selecting only one variable as a predictor along with its classification rate. The significant variables are starred.

The attribute and the accuracy metrics for the first 7 variables are shown next. Notice that once accuracy reaches 67.61% it does not decrease:

The call to the function is shown in the log, and the Decision Tree rules are displayed.

Interpreting...

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