Combining predictions from linear models when training and test inputs differ

24 Jun 2014 Thijs van Ommen

Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs. Many of these methods (often implicitly) make the assumption that the test inputs are identical to the training inputs, which is seldom reasonable... (read more)

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