# Document classification with Bayes

I want to build a document classifier in R, using the Naive Bayes approach.

Here are steps, that I've done so far:

• I have corpus with about 30 documents from 2 authors (Classes are: "target author" and "other author").
• "Vocabulary" (training set) has been pre-processed (removed numbers, removed punctuation, words to lower case, removed stop words, stem documents, strip whitespace), and I am considering only frequent words (top 700).
• Now I have matrix which looks like:

Then I trained my classifier using Bayes using some existing R library, e1071.

Here are my questions:

I want to test my classifier on other documents that were not part of the training set.

• How to prepare my data matrix? What if those other documents don't contain all the words (attributes) from my training set? Should I put dummy columns there (e.g., with value=0)?
• Does the position of the words (columns order) matter?

Here is an example:

Training attributes:

"wild"  "wind"  "woman"


Testing attributes:

"woman" "wind" "wild"


Is this ok, or should columns be in the same order as in training matrix?

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## migrated from stackoverflow.comJan 11 '12 at 21:22

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"Classifying documents with Bayes" sounds like you'd like to resurrect the poor reverend and make him help you sort a pile of papers... –  mbq Jan 13 '12 at 13:53
This question is off-topic because it is about necromancy. –  Marc Claesen Apr 24 at 7:44