{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14"],"submitter":["Gallitto G"],"funding":["Deutsche Forschungsgemeinschaft"],"pubmed_abstract":["<h4>Background</h4>Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data preprocessing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs.<h4>Results</h4>Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g., preregistration)"],"journal":["GigaScience"],"pagination":["giaf036"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12077397"],"repository":["biostudies-literature"],"pubmed_title":["External validation of machine learning models-registered models and adaptive sample splitting."],"pmcid":["PMC12077397"],"pubmed_authors":["Kotikalapudi R","Hoffschlag K","Li J","Gallitto G","Bingel U","Kincses B","Englert R","Spisak T"],"additional_accession":[]},"is_claimable":false,"name":"External validation of machine learning models-registered models and adaptive sample splitting.","description":"<h4>Background</h4>Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data preprocessing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs.<h4>Results</h4>Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g., preregistration)","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2026-06-02T17:56:41.874Z","creation":"2026-04-18T03:11:57.463Z"},"accession":"S-EPMC12077397","cross_references":{"pubmed":["40366867"],"doi":["10.1093/gigascience/giaf036"]}}