[[2015__NeurIPS__Hidden Technical Debt in Machine Learning Systems|Ebner+,NeurIPS2015]]にCACEの原則として次のように記載されている。 > Machine learning systems mix signals together, entangling them and making iso- lation of improvements impossible. > ... > No inputs are ever really independent. We refer to this here as the CACE principle: Changing Anything Changes Everything. CACE applies not only to input signals, but also to hyper-parameters, learning settings, sampling methods, convergence thresholds, data selection, and essentially every other possible tweak.