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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

Evaluating the accuracy of a rule


Three main metrics have been developed that measure the importance, or accuracy of an association rule: support, confidence, and lift.

Support

Support measures how frequently the items occur together. Imagine having a shopping cart in which there can be a very large number of combinations of items. Some items that occur rarely could be excluded from the analysis. When an item occurs frequently you will have more confidence in the association among the items, since it will be a more popular item. Often your analysis will be centered around items with high support.

Calculating support

Calculating support is simple. You first calculate a proportion by counting the number of times that the items in the rule appear in the basket divided by the total number of occurences in the itemsets:

Examples

  • We can see that for the first rule (index #63), {bottled water} and {tropical fruit} appear together in the same transaction in two different transactions (2 and 3), therefore...
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