Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles

arXiv: Learning, Volume abs/1711.05482, 2017.

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For many applications, an ensemble of base classifiers is an effective solution. The tuning of its parameters(number of classes, amount of data on which each classifier is to be trained on, etc.) requires G, the generalization error of a given ensemble. The efficient estimation of G is the focus of this paper. The key idea is to approxima...More



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