{"id":"W3040858639","doi":"10.24963/ijcai.2020/662","title":"Learning Optimal Decision Trees using Constraint Programming (Extended Abstract)","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"","keywords":"Computer science; Constraint programming; Constraint (computer-aided design); Key (lock); Decision tree; Artificial intelligence; Machine learning; Greedy algorithm; Constraint satisfaction; Limit (mathematics); Incremental decision tree; Mathematical optimization; Decision tree learning; Mathematics; Algorithm; Stochastic programming","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":[],"consensus_categories":[],"category_scores_codex":[0.0001532167,0.0001062844,0.0001146687,0.0000344259,0.0002015106,0.0003731152,0.0005350188,0.00003688342,0.0000440911],"category_scores_gemma":[0.00008005682,0.00009432947,0.00004698543,0.0003106212,0.00004548795,0.0004183371,0.0002527435,0.0001698113,0.00006301629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001585648,"about_ca_system_score_gemma":0.00005925091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002586907,"about_ca_topic_score_gemma":0.000001823343,"domain_scores_codex":[0.9989652,0.00001108934,0.0002124402,0.0003867373,0.0001977719,0.0002267689],"domain_scores_gemma":[0.9994102,0.0000880689,0.00007152413,0.0002212813,0.00004724906,0.0001616887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000015936,0.00002747722,0.00004877058,0.000002515755,0.000006262995,0.000010016,0.0005442383,0.002252197,0.001651534,0.004908984,0.000101615,0.9904448],"study_design_scores_gemma":[0.0001851982,0.00008548796,0.0007759872,0.00001673013,0.000004649844,0.00002265197,0.0003386308,0.9800754,0.0008502729,0.0001027154,0.01737773,0.0001645927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.058273,0.00001785728,0.939817,0.0007016732,0.00003860174,0.000105308,0.000001886109,0.0003323998,0.0007122494],"genre_scores_gemma":[0.4075222,0.000001908298,0.5922908,0.0001122772,0.00004181281,0.000004710632,0.000003652123,0.000004901232,0.00001777497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9902802,"threshold_uncertainty_score":0.3846644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04065732384596839,"score_gpt":0.2948347846810381,"score_spread":0.2541774608350698,"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."}}