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Learning Probabilistic Graphical Models in R

You're reading from   Learning Probabilistic Graphical Models in R Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781784392055
Length 250 pages
Edition 1st Edition
Languages
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Toc

The Naive Bayes model

The Naive Bayes model is one of the most well-known classification models used in machine learning. Despite its simple appearance, this model is very powerful and gives good results with little effort. Of course, when considering the problem of classification, one should not always stay with one model, such as Naive Bayes, but should try out many examples to see which one is the best with a particular dataset.

Classification is an important problem in machine learning and it could be defined as the task of associating observations to a particular class. Let's say we have a dataset with n variables and we assign a class to each data point. The class could be {0,1} or {a,b,c,d}, {red, blue, green, yellow}, or {warm, cold}, and so on. We will see that it is sometimes easier to consider binary classification problems where one has only two classes. But most classification models can be extended to more than two classes.

For example, given physiological characteristics...

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