{"id":"W2990678853","doi":"10.1007/s10601-020-09312-3","title":"Learning optimal decision trees using constraint programming","year":2020,"lang":"en","type":"article","venue":"Constraints","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"","keywords":"Constraint programming; Computer science; Decision tree; Constraint (computer-aided design); Machine learning; Artificial intelligence; Key (lock); Constraint satisfaction; Incremental decision tree; Greedy algorithm; Constraint learning; Decision tree learning; Mathematical optimization; Constraint logic programming; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00444465,0.001475142,0.003050035,0.002267297,0.0009900843,0.003267269,0.002508789,0.001964298,0.005699894],"category_scores_gemma":[0.02609738,0.001605763,0.001892426,0.004825234,0.001294244,0.004227702,0.001464882,0.003924933,0.000891819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001554552,"about_ca_system_score_gemma":0.003016691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006680328,"about_ca_topic_score_gemma":0.009106113,"domain_scores_codex":[0.9959186,0.001949827,0.0002850078,0.0008064297,0.0007945042,0.0002456225],"domain_scores_gemma":[0.9680123,0.0290885,0.0008985798,0.000632166,0.001060537,0.0003079447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001730332,0.0002692836,0.001418678,0.0006124792,0.0002031524,0.0001795413,0.0001069815,0.7460088,0.0006207197,0.05335309,0.010361,0.1866933],"study_design_scores_gemma":[0.00002869451,0.00001955949,0.00007035323,0.00004706764,0.0000212447,0.00002687181,0.00002136537,0.9420699,0.0002774405,0.0563898,0.001017531,0.00001022846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01012498,0.0009365864,0.9854347,0.000623606,0.00005946488,0.0001414748,0.0005168674,0.0003225306,0.00183982],"genre_scores_gemma":[0.1945985,0.001341172,0.7983378,0.0003676067,0.0001783994,0.0006147668,0.0024051,0.0002703527,0.001886221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006680328,"threshold_uncertainty_score":0.02350581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04206220394095465,"score_gpt":0.2883871106859103,"score_spread":0.2463249067449557,"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."}}