Averaging SVM and GLM results: sensible or stupid? I have taken two different approaches to calculate probability: using a GLM and an SVM. They are giving slightly different results (which is understandable, they are completely different approaches). Both methods are proving to be generally accurate though and doing a similar job with predictions.
Given that it's hard to pick which method is better, I was just wondering if there would be any value in averaging the two results eg if item A has probably 0.65 of occurring according to SVM and 0.71 according to GLM, then take 0.68 as the probability.
Does this sound ok - getting the benefit of both a frequentist approach and a ML approach? Or is it just bad stats with no real basis?

This is (potentially) fine.  You are describing a simple version of model averaging.  Of course, whether it is actually better in your case is an empirical question.

For what it's worth, "frequentist" doesn't really contrast with "machine learning".  In statistics, frequentist would contrast with Bayesian, but that distinction is orthogonal to this question.  You could contrast statistics with machine learning (many people do), but I think the distinction is somewhat forced and artificial, and at any rate, it's orthogonal to this question.
