e-fold cross-validation: A computing and energy-efficient alternative to k-fold cross-validation with adaptive folds

We present the idea of "e-fold" cross-validation. The core idea is that e is chosen ’intelligently’ and individually for each experiment and dataset. This contrasts a static k chosen by gut feeling and past experiences on what k is ’typically’ good. Our goal for e-fold cross-validation is that e is as small as possible so as not to waste timeand energy and not to create unnecessary CO2 emissions but large enough to provide (near) optimal performance.