An official website of the United States government
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Many machine learning models have tuning parameters to be determined by the training data, and cross‐validation (CV) is perhaps the most commonly used method for selecting tuning parameters. This work concerns the problem of estimating the generalization error of a CV‐tuned predictive model. We propose to use an honest leave‐one‐out cross‐validation framework to produce a nearly unbiased estimator of the post‐tuning generalization error. By using the kernel support vector machine and the kernel logistic regression as examples, we demonstrate that the honest leave‐one‐out cross‐validation has very competitive performance even when competing with the state‐of‐the‐art .632+ estimator.more » « less
