Bootstrap Bias Corrected Cross Validation applied to Super Learning
Abstract
Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. We apply this idea to verification of super learner and compare with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.
Cite
@article{arxiv.2003.08342,
title = {Bootstrap Bias Corrected Cross Validation applied to Super Learning},
author = {Krzysztof Mnich and Agnieszka Kitlas Golińska and Aneta Polewko-Klim and Witold R. Rudnicki},
journal= {arXiv preprint arXiv:2003.08342},
year = {2020}
}
Comments
14 pages, 4 tables, 1 figure, submitted to International Conference on Computational Science, Amsterdam 2020