The requirement that learning was successful in the sense of (6a) is not less restrictive. On the one hand, to obtain (6a) one must choose the loss function l in (2) such that the learned function f coincides with the conditional expectation; for example, l could be chosen to be the squared Euclidean distance for a real-valued response variable Y . On the other hand, learning must be successful in the sense that f interpolates and extrapolates well, i.e., (6a) should hold not only for (x, t) tuples in the dataset D, but also for (x, t) combinations not seen during training (i.e., model generalization). This is important not only in 125 the standard setting of meteorological normalization (e.g., because due to random resampling there will be (r, t) combinations not seen during training), but especially also if meteorological normalization is used in forecasting settings, i.e., in settings where T includes time indicators and M (t) is evaluated for t outside the training range.