Agree: I had a dataset for work no one had yet been able to use in categorizing two effects (one category was 98% of all the data). The values looked too "Gaussian normal" with everything mixed up. It couldn't be separated out, but a combination of SVM and in dept knowledge of the source of the data and I was able to find a generalized model that could accurately categorize parts 80%+ of the time for the small set, without misclassifying the other 98%. All other methodologies had failed up to that point and a blind approach with linear regression or SVMs resulted in at best 70% accuracy on all categories... not very good or implementable in a production setting (that means in the bulk of cases the 98% I was only correct 70% of the time).