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Learning Bayesian Models with R

You're reading from   Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

Exercises


  1. In this exercise, we will use the DBWorld e-mails dataset from the UCI Machine Learning repository to compare the relative performance of Naïve Bayes and BayesLogit methods. The dataset contains 64 e-mails from the DBWorld newsletter and the task is to classify the e-mails into either announcements of conferences or everything else. The reference for this dataset is a course by Prof. Michele Filannino (reference 5 in the References section of this chapter). The dataset can be downloaded from the UCI website at https://archive.ics.uci.edu/ml/datasets/DBWorld+e-mails#.

    Some preprocessing of the dataset would be required to use it for both the methods. The dataset is in the ARFF format. You need to download the foreign R package (http://cran.r-project.org/web/packages/foreign/index.html) and use the read.arff( ) method in it to read the file into an R data frame.

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