find_predictorsthat finds the most features that weight most for and against the category that a given object is eventually classified under. The simple algorithm assumes that there are only two categories - but it should be possible to extend it for more categories. The returned numbers are hard to interpret - but what is important is how big they are in comparison with other numbers in the result.
We use the classifier for spam detection - whenever we get misclassified posts I check what words (or other features) push them into the wrong category and I decide what to do - should be improve the training examples, add more post features or maybe we can just ignore the case. When improving the filters, by adding or removing examples I can check how that changes the classification and also how exactly it changes the influence of each important feature on the result.