{"id":"W2021881393","doi":"10.1142/s0218001410007877","title":"LEARNING DECISION TREES WITH LOG CONDITIONAL LIKELIHOOD","year":2010,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Concordia University; University of Alberta","funders":"","keywords":"Machine learning; Artificial intelligence; Decision tree; Computer science; Conditional probability; Tree (set theory); Naive Bayes classifier; Bayes' theorem; Estimator; Bayesian network; Bayesian probability; Mathematics; Support vector machine; Statistics","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":[0.01226345,0.001794921,0.002841304,0.003162466,0.001134328,0.002723744,0.003344963,0.002805003,0.003576679],"category_scores_gemma":[0.03991523,0.001317872,0.002047754,0.003130922,0.00119884,0.005571145,0.00200178,0.004179556,0.001521835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001934574,"about_ca_system_score_gemma":0.002394508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006581483,"about_ca_topic_score_gemma":0.007372477,"domain_scores_codex":[0.9942007,0.003324161,0.0003788472,0.000858398,0.0009172111,0.0003206694],"domain_scores_gemma":[0.960754,0.03390869,0.001267481,0.00152536,0.002049234,0.0004952971],"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":[0.0003639823,0.0002140572,0.005759682,0.0002174808,0.0001697753,0.0001422331,0.0002050762,0.7967772,0.000364325,0.02382176,0.007441263,0.1645232],"study_design_scores_gemma":[0.00002301642,0.00002666349,0.0001450993,0.00002268205,0.00001207068,0.00001981432,0.00001251139,0.9721815,0.0001467156,0.02687221,0.0005270108,0.0000107103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02221069,0.001225918,0.9719891,0.0007923271,0.00009365041,0.0001860022,0.0005158786,0.001724845,0.001261663],"genre_scores_gemma":[0.43723,0.0009533351,0.554096,0.0008528702,0.0002822386,0.0005930026,0.003194209,0.0003048091,0.002493451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01226345,"threshold_uncertainty_score":0.06485605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535984494031065,"score_gpt":0.2982625570619071,"score_spread":0.2529027121215964,"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."}}