{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01570114,0.001356262,0.002538387,0.003616269,0.0009167106,0.003895559,0.003396062,0.002847507,0.003247054],"category_scores_gemma":[0.029812,0.0009916414,0.001073839,0.005890758,0.005283626,0.007608023,0.002760042,0.00485586,0.0034689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328602,"about_ca_system_score_gemma":0.001755391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001027017,"about_ca_topic_score_gemma":0.001074987,"domain_scores_codex":[0.9891688,0.006044459,0.0004194303,0.001656204,0.002504636,0.0002066259],"domain_scores_gemma":[0.9796094,0.01636512,0.00061669,0.001649437,0.001556692,0.0002026175],"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":[0.00008972592,0.00004193919,0.0006833437,0.002303473,0.0002163775,0.00006471585,0.0001663256,0.009040095,0.0004247917,0.4003271,0.01574335,0.5708988],"study_design_scores_gemma":[0.00003764037,0.00008342188,0.0006779225,0.001141429,0.00008555326,0.0003244105,0.00007108461,0.02377754,0.001300111,0.783742,0.1886763,0.00008249317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001842238,0.4948066,0.4746279,0.008629525,0.00198633,0.00008017084,0.0002000541,0.0004137984,0.01741338],"genre_scores_gemma":[0.1740662,0.5108637,0.2865801,0.005951097,0.009201887,0.0004898996,0.0007017113,0.0005935773,0.01155201],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01570114,"threshold_uncertainty_score":0.08303648,"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."}}