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Selecting learning machines such as classifiers is an important task whenusing AI in the clinic. K-fold crossvalidation is a practical technique thatallows simple inference of such machines. However, the recipe generates manymodels and does not provide a means to determine the best one. In this paper, amodified recipe is presented, that generates more consistent machines withsimilar on-average performance, but less extra-sample loss variance and lessfeature bias. A use case is provided by applying the recipe onto the atrialflutter localization problem.

Selecting Classifiers by Pooling over Cross-Validation Results in More Consistency in Small-Sample Classification of Atrial Flutter Localization