With Random Forests, one can estimate the prediction error using out-of-bag simulations. So for every sample in the training/test-set, one can estimate the predictive uncertainty.
- What would be the equivalent way to measure predictions error in Neural Nets?
- Does the oob-error quantify epistemic (model) of aleatoric (data) uncertainty?
- Can MC dropout here be seen as similar to oob-error?