Cross-validation with Confidence

Neyman Seminar
Apr 26, 2017 4:00pm to 5:00pm
Location: 
1011 Evans Hall
Status: 
Happening As Scheduled
Cross-validation is one of the most popular model selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, unless the ratio between the training and testing sample sizes is very small. We argue that such an overfitting tendency of cross-validation is due to the ignorance of the uncertainty in the testing...
Jing Lei, Department of Statistics, CMU