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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
Languages
Tools
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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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Toc

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

Calculating goodness of fit measures

Confusion matrix

We can compute the confusion, or error, matrix in order to determine how our manual calculation performed, when we classified the prediction outcomes as correct or not:

#Confusion matrix 
result <- sql("select outcome,correct, count(*) as k, avg(totrows) as totrows from preds_tbl where grp=1 group by 1,2 order by 1,2")
result$classify_pct <- result$k/result$totrows

display(result)

To determine the grand total correct model prediction, sum the correct=Y columns previously:

Summary of correct predictions for training group:

Correctly predicted outcome=1

20%

Correctly predicted outcome=0

59%

Total Correct Percentage

79%

 

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