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.
Learning Bayesian Models with R
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Learning Bayesian Models with R
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Overview of this book
Table of Contents (16 chapters)
Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introducing the Probability Theory
The R Environment
Introducing Bayesian Inference
Machine Learning Using Bayesian Inference
Bayesian Regression Models
Bayesian Classification Models
Bayesian Models for Unsupervised Learning
Bayesian Neural Networks
Bayesian Modeling at Big Data Scale
Index
Customer Reviews