I'm using support vector regression (not classification) for a problem and it's working well.
However, in the older method that former lab members developed (a basic linear model, with weights determined just from OLS), there is a lot of code used to determine the individual efficacy of each different right-hand-side variable from their OLS. I'm not all that impressed with these efficacy stats (they are mostly summary stats of correlations, t-stats, etc.) but they do allow you to have some notion of which RHS variables are most informative.
In my case, for each individual datum, I use the whole row's worth of right-hand-side variables as the feature vector for support vector regression. So I am wondering if there is any commonly used analogue, maybe some measure from information theory or something, that gives an indication of how each marginal component of the overall feature vector contributes to the end performance.
One thing that does not seem to work is training N different regressions each using only a single one of the feature components as the sole predictor. This does give some matches between the univariate regression efficacy and univariate support vector efficacy, but it doesn't capture the interactions between the different predictors, and (for example) which predictor is the "second best" predictor among all the components of the feature vector.
I suppose I could work backward and test the results of removing one component at a time, to see which then causes the biggest drop in performance... but I'd prefer something less ad-hoc and perhaps backed by some statistical rigor or intuition.