{"id":"W1939513865","doi":"10.1002/wics.1362","title":"Use of majority votes in statistical learning","year":2015,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Popularity; Gradient boosting; Computer science; Cluster analysis; Random forest; Machine learning; Artificial intelligence; Aggregate (composite); Exploratory data analysis; Ensemble learning; Data science; Data mining; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002277675,0.0008287212,0.004860363,0.0003901396,0.0001132117,0.00009146384,0.0005057966,0.0003402471,0.0003777258],"category_scores_gemma":[0.009994639,0.0006731281,0.0003560022,0.0006550735,0.000388962,0.00013763,0.0008972684,0.001298726,0.0001404809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539746,"about_ca_system_score_gemma":0.0005287018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002566418,"about_ca_topic_score_gemma":0.00002278628,"domain_scores_codex":[0.9916112,0.002629667,0.00370797,0.0007716768,0.0007627128,0.000516756],"domain_scores_gemma":[0.9791006,0.01792603,0.001739293,0.0004575449,0.0004899892,0.0002865297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001671956,0.0002291516,0.0000228508,0.02138965,0.00007390567,0.00006477819,0.0001420054,0.00008465232,1.590029e-8,0.1681834,0.01644675,0.7933461],"study_design_scores_gemma":[0.0001738397,0.0003066568,0.00002417538,0.02416946,0.0005590664,0.00005938977,0.0000237789,0.007358074,1.78697e-8,0.4952524,0.4714682,0.0006049639],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.596128e-7,0.521097,0.4746526,0.000006574641,0.0002072063,0.0009434069,0.002912291,0.00002820899,0.0001518343],"genre_scores_gemma":[0.000001405297,0.515549,0.4832236,0.000007174987,0.00006708643,0.0001026033,0.0008920213,0.00005995853,0.0000971043],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7927411,"threshold_uncertainty_score":0.999572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3781482536796996,"score_gpt":0.4937488496305504,"score_spread":0.1156005959508508,"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."}}