Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation

23 Aug 2017Ioannis TsamardinosElissavet GreasidouMichalis TsagrisGiorgos Borboudakis

Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically biased... (read more)

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