Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 115, Issue 11, Pages 2578-2583Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1708283115
Keywords
ensemble learning; replicability; cross-study validation; machine learning; validation
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Funding
- National Cancer Institute (NCI) Training Grant [2T32CA009337-36]
- NCI Core Grant [4P30CA006516-51]
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This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations.
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