کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
416968 | 681424 | 2011 | 17 صفحه PDF | دانلود رایگان |
Reliable estimation of the classification performance of inferred predictive models is difficult when working with small data sets. Cross-validation is in this case a typical strategy for estimating the performance. However, many standard approaches to cross-validation suffer from extensive bias or variance when the area under the ROC curve (AUC) is used as the performance measure. This issue is explored through an extensive simulation study. Leave-pair-out cross-validation is proposed for conditional AUC-estimation, as it is almost unbiased, and its deviation variance is as low as that of the best alternative approaches. When using regularized least-squares based learners, efficient algorithms exist for calculating the leave-pair-out cross-validation estimate.
Journal: Computational Statistics & Data Analysis - Volume 55, Issue 4, 1 April 2011, Pages 1828–1844