{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["26(2)"],"submitter":["O'Connell NS"],"pubmed_abstract":["Random forest (RF) regression is popular machine learning method to develop prediction models for continuous outcomes. Variable selection, also known as feature selection or reduction, involves selecting a subset of predictor variables for modeling. Potential benefits of variable selection are methodologic (i.e. improving prediction accuracy and computational efficiency) and practical (i.e. reducing the burden of data collection and improving efficiency). Several variable selection methods leveraging RFs have been proposed, but there is limited evidence to guide decisions on which methods may be preferable for different types of datasets with continuous outcomes. Using 59 publicly available datasets in a benchmarking study, we evaluated the implementation of 13 RF variable selection method"],"journal":["Briefings in bioinformatics"],"pagination":["bbaf096"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11891652"],"repository":["biostudies-literature"],"pubmed_title":["A comparison of random forest variable selection methods for regression modeling of continuous outcomes."],"pmcid":["PMC11891652"],"pubmed_authors":["Speiser JL","Bullock GS","O'Connell NS","Jaeger BC"],"additional_accession":[]},"is_claimable":false,"name":"A comparison of random forest variable selection methods for regression modeling of continuous outcomes.","description":"Random forest (RF) regression is popular machine learning method to develop prediction models for continuous outcomes. Variable selection, also known as feature selection or reduction, involves selecting a subset of predictor variables for modeling. Potential benefits of variable selection are methodologic (i.e. improving prediction accuracy and computational efficiency) and practical (i.e. reducing the burden of data collection and improving efficiency). Several variable selection methods leveraging RFs have been proposed, but there is limited evidence to guide decisions on which methods may be preferable for different types of datasets with continuous outcomes. Using 59 publicly available datasets in a benchmarking study, we evaluated the implementation of 13 RF variable selection method","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-04T08:23:09.609Z","creation":"2025-04-04T08:23:09.609Z"},"accession":"S-EPMC11891652","cross_references":{"pubmed":["40062620"],"doi":["10.1093/bib/bbaf096"]}}