I also tried polynomial kernels of order=3 with costs around 0.1, as well as many different gammas for the radial kernel. No luck. As I said, the conversion to probabilities could be the culprit.
However, the predictive performance of SVM is irrelevant to the main points I am trying to make. In other words, even if SVM beat GAM in this single test, it does not invalidate the highlighted benefits of GAM. I would argue that GAM poccesses qualities that SVMs do not, and vice versa.
Feel free to suggest different SVM settings, or a better way to convert classifications into a continuous measure, and I will change the content in the comparison table. The data and code can be downloaded here: https://github.com/klarsen1/gampost.
