For which kinds of supervised machine learning techniques is it possible to estimate how uncertain the model is about its predictions for given level/range of the predictor once the model is trained on a set of data?
I can imagine that it is possible to do that e.g. for random forest by looking at the variance of votes that the forest is giving for a data point in the evaluation dataset. On the other hand it seems for me impossible to estimate model uncertainty for linear regression and similar methods.
Could anyone explain for which machine learning techniques this can be done or point me to the relevant literature?