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Coaching variables for regression and classification
Authors:ROBERT TIBSHIRANI  GEOFFREY HINTON
Affiliation:(1) Department of Public Health Sciences and Department of Statistics, University of Toronto, Toronto, Ontario, Canada;(2) Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
Abstract:In a regression or classification setting where we wish to predict Y from x1,x2,..., xp, we suppose that an additional set of lsquocoachingrsquo variables z1,z2,..., zm are available in our training sample. These might be variables that are difficult to measure, and they will not be available when we predict Y from x1,x2,..., xp in the future. We consider two methods of making use of the coaching variables in order to improve the prediction of Y from x1,x2,..., xp. The relative merits of these approaches are discussed and compared in a number of examples.
Keywords:regression  classification  missing data  mixtures of experts
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