  Regression analysis is a standard supervised machine learning method used to
model an outcome variable in terms of a set of predictor variables. In most
real-world applications we do not know the true value of the outcome variable
being predicted outside the training data, i.e., the ground truth is unknown.
It is hence not straightforward to directly observe when the estimate from a
model potentially is wrong, due to phenomena such as overfitting and concept
drift. In this paper we present an efficient framework for estimating the
generalization error of regression functions, applicable to any family of
regression functions when the ground truth is unknown. We present a theoretical
derivation of the framework and empirically evaluate its strengths and
limitations. We find that it performs robustly and is useful for detecting
concept drift in datasets in several real-world domains.
