PyML: machine learning in Python

I am currently playing around with it. Decent support vector machine implementation. However, I have some problems with it. It is by far not as hugh as weka <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow">http://www.cs.waikato.ac.nz/ml/weka/</a> It is in python (big plus) and seems to be easily hacked for online classification. I like <a href="https://mlpy.fbk.eu/" rel="nofollow">https://mlpy.fbk.eu/</a> a bit better, as it has also a decent integrated Nearest Neighbor (ok, ok this one is not hard to implement on your own) and FDA + DWT (awsome).

PySVM might have been a better name, since it looks like it only does SVM and kernel methods. I haven't used this package, but I'll recommend libsvm and liblinear because they're fast and have wrappers for just about every language you want, including Java, Ruby, Matlab, and Python <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/#python" rel="nofollow">http://www.csie.ntu.edu.tw/~cjlin/libsvm/#python</a><p>If you're doing large scale linear classification, liblinear is especially awesome and fast. <a href="http://www.csie.ntu.edu.tw/~cjlin/liblinear/" rel="nofollow">http://www.csie.ntu.edu.tw/~cjlin/liblinear/</a><p>EDIT: From the PyML documentation:<p>"By default the libsvm solver is used in training. To use the PyML SMO optimizer either set the optimizer attribute to ’mysmo’ or instantiate an svm instance as svm.SVM(optimizer = ’mysmo’). Note that for a non-vector dataset, the default libsvm optimizer cannot be used and the PyML native SMO implementation is automatically used instead (it is slower than libsvm so is not the default)"

One serious problem of HN is that the amount of good stuff on here greatly exceeds my capacity for absorbing it all... Roughly one week goes by and I have at least another months worth of reading added to my list.

<a href="http://www.ailab.si/orange/" rel="nofollow">http://www.ailab.si/orange/</a><p>This is the most thorough Python machine learning package I know of. Includes SVM.<p>Lots of tutorials, too, and a visual development environment (if that's your style).

If only there were some good non-GPL machine learning libraries for Python...

Has anyone used this? Care to share your experience?