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.

authors

Patil P,Parmigiani G

doi

10.1073/pnas.1708283115

subject

Has Abstract

pub_date

2018-03-13 00:00:00

pages

2578-2583

issue

11

eissn

0027-8424

issn

1091-6490

pii

1708283115

journal_volume

115

pub_type

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