Most methods for performing regressions don't provide natural uncertainties. Some do, of course! But few deliver uncertainties you will believe. I discussed these issues with Contardo (SISSA) today, in the context of our project to (confidently) find infrared excesses around boring old main-sequence stars. One option is to look at the performance on held-out data. But then you have to decide how to aggregate this information in a way that is relevant for each object in your sample: They probably don't all have the same uncertainty! Another option is to look at the variation of prediction across training sets. That's good! But it requires that you have lots of training data. In this case, we do, so that's where we are at right now.
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