{"id":"W3142903789","doi":"10.1287/opre.2021.0034","title":"Strong Optimal Classification Trees","year":2024,"lang":"en","type":"article","venue":"Operations Research","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Flexibility (engineering); Heuristic; Computer science; Mathematical optimization; Integer (computer science); Decision tree; Machine learning; Revenue; Integer programming; Ranging; Artificial intelligence; Tree (set theory); Sample (material); Relaxation (psychology); Operations research; Mathematics; Statistics; Economics","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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001277219,0.0000667708,0.00005491717,0.0003559516,0.000485061,0.001794653,0.0006504489,0.00004804028,0.0001088659],"category_scores_gemma":[0.0001924182,0.00005749421,0.00002735376,0.001026921,0.00006596675,0.0009506791,0.0001531458,0.0004229553,0.001354656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008091354,"about_ca_system_score_gemma":0.0002556279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001176367,"about_ca_topic_score_gemma":0.0001004613,"domain_scores_codex":[0.9984177,0.000282997,0.0001531567,0.0004214408,0.0004801673,0.0002445342],"domain_scores_gemma":[0.9989398,0.0001560039,0.000004754637,0.0006358189,0.0001872643,0.00007639603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001092718,0.00003213864,0.00006770531,0.000008817813,0.000006690294,0.000004202689,0.0004230879,0.003303655,0.006611299,0.8600097,0.005850029,0.1236816],"study_design_scores_gemma":[0.00003732477,0.00003945555,0.003725104,0.00001490191,0.000001077998,0.000005737223,0.0001051305,0.9088482,0.0002553501,0.0001497334,0.08675541,0.00006253313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009265702,0.0004726766,0.9495042,0.01709708,0.0002216876,0.0002153754,0.000008856019,0.0003958821,0.02281855],"genre_scores_gemma":[0.9637838,0.00006392779,0.02696962,0.00001762091,0.0001469038,0.00009805034,0.00008708851,0.000008963043,0.008824056],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9545181,"threshold_uncertainty_score":0.9994229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1515426922793801,"score_gpt":0.4452963514217984,"score_spread":0.2937536591424184,"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."}}