{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003091565,0.001079676,0.001427569,0.0009772065,0.0006884148,0.001914954,0.00127196,0.001655839,0.008100512],"category_scores_gemma":[0.0103769,0.0006329064,0.001245617,0.001317557,0.001107233,0.00256697,0.001864477,0.002782957,0.001798823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009781329,"about_ca_system_score_gemma":0.001709398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009688298,"about_ca_topic_score_gemma":0.001799173,"domain_scores_codex":[0.9978155,0.0009353574,0.0001291367,0.000366697,0.0004763253,0.0002769537],"domain_scores_gemma":[0.9957639,0.002719958,0.0003216462,0.0003493302,0.00067341,0.0001717419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001868962,0.0001619338,0.00128947,0.0004076317,0.00008452582,0.0001750568,0.0001353821,0.3926856,0.001320887,0.3819152,0.03086714,0.1907703],"study_design_scores_gemma":[0.00003659114,0.00005557862,0.0002005204,0.0000716816,0.00001741247,0.00004781135,0.00002902334,0.7378184,0.0003559451,0.2540574,0.00729985,0.000009811407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01594409,0.001663786,0.9616278,0.001652258,0.0002489013,0.0001312991,0.0006267817,0.0003436855,0.01776144],"genre_scores_gemma":[0.3922187,0.002259116,0.5825747,0.001828058,0.0008069367,0.0006208678,0.002466159,0.0003776201,0.01684789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008100512,"threshold_uncertainty_score":0.02709889,"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."}}