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I have tried the Orange Framework for Naive Bayesian classification. The methods are extremely unintuitive, and the documentation is extremely unorganized. Does anyone here have another framework to recommend?
I use mostly NaiveBayesian for now. I was thinking of using nltk's NaiveClassification but then they don't think they can handle continuous variables.
What are my options?
The scikit-learn has an implementation of Gaussian naive Bayesian classifier. In general, the goal of this library is to provide a good trade off between code that is easy to read and use, and efficiency. Hopefully it should be a good library to learn of the algorithms work.
This might be a good place to start. It's the full source code (the text parser, the data storage, and the classifier) for a python implementation of of a naive Bayesian classifier. Although it's complete, it's still small enough to digest in one session. I think the code is reasonably well written and well commented. This is part of the source code files for the book Programming Collective Intelligence.
To get the source, click the link, dl and unpack the zip, from the main folder 'PCI_Code', go to the folder 'chapter 6', which has a python source file 'docclass.py. That's the complete source code for a Bayesian spam filter. The training data (emails) are persisted in an sqlite database which is also included in the same folder ('test.db') The only external library you need are the python bindings to sqlite (pysqlite); you also need sqlite itself if you don't already have it installed).
If you're processing natural language, check out the Natural Language Toolkit.
If you're looking for something else, here's a simple search on PyPI.
pebl appears to handle continuous variables.
I found Divmod Reverend to be the simplest and easiest to use Python Bayesian classifier.
I just took Paul Graham's LISP stuff and converted to to Python
http://www.paulgraham.com/spam.html
There’s also SpamBayes, which I think can be used as a general naive Bayesian clasisfier, instead of just for spam.
