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As the main workhorse for model selection, Cross Validation (CV) has achievedan empirical success due to its simplicity and intuitiveness. However, despiteits ubiquitous role, CV often falls into the following notorious dilemmas. Onthe one hand, for small data cases, CV suffers a conservatively biasedestimation, since some part of the limited data has to hold out for validation.On the other hand, for large data cases, CV tends to be extremely cumbersome,e.g., intolerant time-consuming, due to the repeated training procedures.Naturally, a straightforward ambition for CV is to validate the models with farless computational cost, while making full use of the entire given data-set fortraining. Thus, instead of holding out the given data, a cheap andtheoretically guaranteed auxiliary/augmented validation is derivedstrategically in this paper. Such an embarrassingly simple strategy only needsto train models on the entire given data-set once, making the model-selectionconsiderably efficient. In addition, the proposed validation approach issuitable for a wide range of learning settings due to the independence of bothaugmentation and out-of-sample estimation on learning process. In the end, wedemonstrate the accuracy and computational benefits of our proposed method byextensive evaluation on multiple data-sets, models and tasks.

Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection