A simple explanation of Naive Bayes Classification

Posted by Jaggerjack on Stack Overflow See other posts from Stack Overflow or by Jaggerjack
Published on 2012-04-08T00:56:19Z Indexed on 2012/08/29 9:39 UTC
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Filed under:

algorithm

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dataset

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machine-learning

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classification

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bayesian

I am finding it hard to understand the process of Naive Bayes, and I was wondering if someone could explained it with a simple step by step process in English. I understand it takes comparisons by times occurred as a probability, but I have no idea how the training data is related to the actual dataset.

Please give me an explanation of what role the training set plays. I am giving a very simple example for fruits here, like banana for example

training set---
round-red
round-orange
oblong-yellow
round-red

dataset----
round-red
round-orange
round-red
round-orange
oblong-yellow
round-red
round-orange
oblong-yellow
oblong-yellow
round-red

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