Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation

: In this paper we prove sanity-check bounds for the error of the leave-one-out cross-validation estimate of the generalization error: that is, bounds showing that the worst-case error of this estimate is not much worse than that of the training error estimate. The name sanity-check refers to the fact that although we often expect the leave-one-out estimate to perform considerably better than the training error estimate, we are here only seeking assurance that its performance will not be considerably worse. Perhaps surprisingly, such assurance has been given only for limited cases in the prior literature on cross-validation. Any nontrivial bound on the error of leave-one-out must rely on some notion of algorithmic stability. Previous bounds relied on the rather strong notion of hypothesis stability, whose application was primarily limited to nearest-neighbor and other local algorithms. Here we introduce the new and weaker notion of error stability, and apply it to obtain sanity-check b...

• Type: Conference
• Year: 1997
• Where: COLT
• Authors: Michael J. Kearns, Dana Ron
• Keywords: COLT 1997, Leave-one-out Estimate, Machine Learning, Training Error, Training Error Estimate