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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

Outliers


Outliers are values in the data that are outside the range of what is to be expected. "What is to be expected?" is of course subjective. Some people will define an outlier as anything beyond three standard deviations of a normal distribution, or anything beyond 1.5 times the interquartile ranges. This, of course, may be good starting points, but there are many examples of real data that defies any statistical explanation. These rules of thumb are also highly dependent upon the form of the data. What might be considered an outlier for a normal distribution would not hold for a lognormal or Poisson distribution.

In addition to potential single variable outliers, outliers can also exist in multivariate form, and are more prevalent as data is examined more closely in a high-dimensional space.

Whenever they appear, outliers should be examined closely since they may be simple errors or provide valuable insight. Again, it is best to consult with other collaborators when you suspect deviation...

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