Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.

ABSTRACT: In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labeled data is not selected uniformly at random. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working

SUBMITTER: Gronsbell J 

PROVIDER: S-EPMC9586151 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature