Sorry for the late reply. Here are the two I can think of off the top of my head: the first is that decision trees (or even naive bayes) produce results that a

Sorry for the late reply. Here are the two I can think of off the top of my head: the first is that decision trees (or even naive bayes) produce results that are intuitively understandable and human readable. In contrast the result of training an SVM is a huge vector of 100's of floats and there's no real way to explain to someone (e.g. someone buying your tech, a manager, etc) what's actually going on. I didn't mention this before but SVM's do get you something that decision trees, naive bayes, and others don't: they will look at all kinds of combinations of attributes. This becomes critical for applications like machine vision where e.g. looking at any one single pixel as an attribute won't do anything for you and so you need something that will understand lots of different possible combinations of attributes. Which is also the second weakness of SVMs: most data sets that aren't images don't express strong patterns across a wide variety of combinations of features. To take naive bayes as an example, it is a hugely popular algorithm and the 'naive' part (which tends to work well on my data sets) is the assumption that each attribute is statistically independent!