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In this paper we explore the possibility of automatic model selection in the supervised learning framework with the use of prediction intervals. First we compare two families of non-parametric approaches of constructing prediction intervals for arbitrary regression models. The first family of approaches is based on the idea of explaining the total prediction error as a sum of the model's error and...
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