  Selecting learning machines such as classifiers is an important task when
using AI in the clinic. K-fold crossvalidation is a practical technique that
allows simple inference of such machines. However, the recipe generates many
models and does not provide a means to determine the best one. In this paper, a
modified recipe is presented, that generates more consistent machines with
similar on-average performance, but less extra-sample loss variance and less
feature bias. A use case is provided by applying the recipe onto the atrial
flutter localization problem.
