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Preprints, Working Papers, ... Year : 2017

Cross-validation improved by aggregation: Agghoo

Abstract

Cross-validation is widely used for selecting among a family of learning rules. This paper studies a related method, called aggregated hold-out (Agghoo), which mixes cross-validation with aggregation; Agghoo can also be related to bagging. According to numerical experiments, Agghoo can improve significantly cross-validation's prediction error, at the same computational cost; this makes it very promising as a general-purpose tool for prediction. We provide the first theoretical guarantees on Agghoo, in the supervised classification setting, ensuring that one can use it safely: at worse, Agghoo performs like the hold-out, up to a constant factor. We also prove a non-asymptotic oracle inequality, in binary classification under the margin condition, which is sharp enough to get (fast) minimax rates.
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Dates and versions

hal-01585595 , version 1 (11-09-2017)

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Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. Cross-validation improved by aggregation: Agghoo. 2017. ⟨hal-01585595⟩
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