scikit.learn provides nice hooks into libsvm that I do not see in other libsvm wrappers. However, one thing we could use is the set of decision values available when using svm_predict_values().

The decision values are especially useful to us because we are using highly imbalanced classes (a big background model and a single-element or very small target set). It is very difficult to calibrate the models to return the correct class, but the scores returned by svm_predict_values() are different enough that they provide the information we need, and we can fuse the scores with other techniques.

I see you added returning probabilities for 0.3. I looked over the code, and I think this change could be similar. I could work on this to some extent, but if you have a suggestion about structuring it and what the most consistent external API would be, I would be grateful. (E.g, add SVC.predict_values() or incorporate into SVC.predict() itself?) If you feel motivated to add it for your own purposes, that would be wonderful, of course.