> From an accuracy standpoint, GAMs are competitive with popular learning techniques such as Random Forest or SVM.
Would be great to get a reference on this. In [1] the authors compared MARS to RF and SVM on several datasets and it didn't look so good.
May be they just got good performance on the one dataset mentioned at the end or did not optimize the parameters of the competing classifiers. I think it is telling that SVM performed worse than a linear classifier.
One should not reports results on a method they do not understand how to use. the SVM parameters are non-sensical. This would not pass basic peer review
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.
Two things. A GAM is a non-linear model and is quite flexible. The degree of non-linearity is achieved by tuning the spline parameter degrees of freedom as well as introducing tensor splines to get non-linear interactions.
Second, the no free lunch theorms really make papers like the above a lot less telling than you might think. All I really get from them is that RF is a good modeling framework to try but for individual problems maybe try boosting on an SVM or a NN model.
