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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:

    enter image description here

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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"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
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migrated from stackoverflow.com Jan 11 '12 at 21:22

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1 Answer

order of variables is not an issue.I guess you are using the actual tokens as variables then randomforest or svm or any other model can understand that using variable names .THe issue can be when you dont have certain tokens in test data you might need to introduce dummy values

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what are tokens? –  Glen Nov 17 '13 at 19:01
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