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Training replicable predictors in multiple studies.

ABSTRACT: 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.

SUBMITTER: Patil P

PROVIDER: S-EPMC5856504 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Training replicable predictors in multiple studies.

Patil Prasad P Parmigiani Giovanni G

Proceedings of the National Academy of Sciences of the United States of America 20180312 11

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 ...[more]

PMID: 29531060

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