{"id":"W2082914115","doi":"10.1007/s10462-007-9055-0","title":"Just enough learning (of association rules): the TAR2 “Treatment” learner","year":2006,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; West Virginia University; National Aeronautics and Space Administration","keywords":"Pruning; Computer science; Set (abstract data type); Association rule learning; Contrast (vision); Class (philosophy); Association (psychology); Simple (philosophy); Machine learning; Domain (mathematical analysis); Artificial intelligence; Controller (irrigation); Psychology; Mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01041839,0.0009344465,0.001954646,0.00216344,0.0009096803,0.002676134,0.004301832,0.002663879,0.005278268],"category_scores_gemma":[0.03309882,0.0005306739,0.001468797,0.002756352,0.002502867,0.009644149,0.003657972,0.007118678,0.001826762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000775793,"about_ca_system_score_gemma":0.001903637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001015536,"about_ca_topic_score_gemma":0.0009976674,"domain_scores_codex":[0.9946944,0.002883986,0.0002905375,0.0007360134,0.001203513,0.000191484],"domain_scores_gemma":[0.9821798,0.01233424,0.0004396352,0.002745947,0.001911565,0.00038881],"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":[0.0004388533,0.0001845336,0.002311574,0.0006561549,0.0002364668,0.0002058619,0.0002894985,0.02165219,0.0006503264,0.4362719,0.03633912,0.5007635],"study_design_scores_gemma":[0.00007753568,0.000155409,0.0003956061,0.0001531456,0.00007845586,0.0004547196,0.00009161622,0.2009043,0.001379795,0.7720352,0.02423817,0.00003599124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00676245,0.00946199,0.9652786,0.009360678,0.0007942133,0.00005764496,0.0002840969,0.0006040169,0.007396231],"genre_scores_gemma":[0.3304878,0.01454978,0.6193068,0.00758177,0.004553709,0.0004752247,0.001826064,0.0005995026,0.0206193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01041839,"threshold_uncertainty_score":0.05509835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06132035250675822,"score_gpt":0.3248625355230162,"score_spread":0.263542183016258,"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."}}