{"id":"W2008044240","doi":"10.1016/j.jclinepi.2007.11.014","title":"Bootstrap model selection had similar performance for selecting authentic and noise variables compared to backward variable elimination: a simulation study","year":2008,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Statistics; Feature selection; Resampling; Regression analysis; Variables; Mathematics; Selection (genetic algorithm); Monte Carlo method; Outcome (game theory); Linear regression; Variable (mathematics); Bootstrap aggregating; Model selection; Regression; Computer science; Econometrics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01835109,0.0001770426,0.001635687,0.0001182705,0.0002263892,0.0000114032,0.0001515581,0.0002002988,0.00002695847],"category_scores_gemma":[0.1339466,0.0001409806,0.0001527437,0.0001963009,0.00008556472,0.000137964,0.00004062908,0.0004894332,0.000001512644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004207209,"about_ca_system_score_gemma":0.0001856698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005715401,"about_ca_topic_score_gemma":0.000002565704,"domain_scores_codex":[0.9942614,0.001676212,0.00324914,0.0002965743,0.0001906569,0.0003260497],"domain_scores_gemma":[0.9383258,0.05901378,0.001500457,0.0001383957,0.0007835966,0.0002379343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00239386,0.002026813,0.2373572,0.0003465761,0.0004134898,0.000005259275,0.001331447,0.6921391,0.0001794116,0.04554539,0.002380258,0.01588118],"study_design_scores_gemma":[0.001170384,0.00193688,0.04506295,0.0000676635,0.0001470849,0.00003786517,0.00005381709,0.8116857,0.000009075625,0.1395942,0.000113551,0.0001207714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4217852,0.00001048258,0.5773942,0.0002823221,0.0001879134,0.0002836433,0.000003385691,0.00001116887,0.00004162163],"genre_scores_gemma":[0.5217522,0.00001915765,0.4777045,0.0002571898,0.0002070086,0.000009181796,5.096482e-7,0.00001001514,0.00004020899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1922942,"threshold_uncertainty_score":0.8733485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5966149628403579,"score_gpt":0.5651412350333136,"score_spread":0.03147372780704427,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}