I am considering using Python libraries for doing my Machine Learning experiments. Thus far, I had been relying on WEKA but have been pretty dissatisfied on the whole. This is primarily because I have found WEKA to be not so well supported (very few examples, documentation is sparse and community support is less than desirable in my experience), and have found myself in sticky situations with no help forthcoming. Another reason I am contemplating this move is because I am really liking Python (I am new to Python), and don't want to go back to coding in Java.
So my question is, what are the more (1) comprehensive (2) scalable (100k features, 10k examples) and (3) well supported libraries for doing ML in Python out there? I am particularly interested in doing text classification, and so would like to use a library that has a good collection of classifiers, feature selection methods (Information Gain, Chi-Sqaured etc.), and text pre-processing capabilities (stemming, stopword removal, tf-idf etc.).
Based on the past e-mail threads here and elsewhere, I have been looking at PyML, scikits-learn and Orange so far. How have people's experiences been with respect to the above 3 metrics that I mention?
Any other suggestions?
Thanks in advance. Andy