{"id":"W2170595610","doi":"10.1109/tkde.2004.1269594","title":"CAIM discretization algorithm","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":449,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Discretization; Discretization of continuous features; Algorithm; Computer science; Decision tree; Class (philosophy); Tree (set theory); ID3 algorithm; Decision tree learning; Artificial intelligence; Incremental decision tree; Mathematics; Discretization error","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":[0.00176727,0.0008299684,0.001390545,0.002071298,0.00109106,0.002850392,0.002671128,0.001993194,0.01429722],"category_scores_gemma":[0.008092287,0.0005263722,0.001575244,0.003063163,0.00071763,0.002193318,0.002139706,0.002620577,0.005243058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344577,"about_ca_system_score_gemma":0.002009822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003637834,"about_ca_topic_score_gemma":0.00307469,"domain_scores_codex":[0.9979939,0.0003345311,0.0002123694,0.0004442981,0.0008521936,0.0001626923],"domain_scores_gemma":[0.9974064,0.00090848,0.0001262579,0.0005806052,0.0009068397,0.00007136902],"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":[0.0002864814,0.0001062742,0.001607387,0.0004224239,0.0001046191,0.0001889156,0.0002417657,0.1309944,0.004943051,0.08943529,0.0501657,0.7215037],"study_design_scores_gemma":[0.0000851648,0.00007579455,0.00051931,0.0000864258,0.00003691103,0.000349854,0.00009625968,0.847951,0.006225958,0.06191643,0.08261566,0.00004110195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002129558,0.0004733619,0.9866919,0.0002338068,0.0002156845,0.0001487855,0.0004903934,0.001423748,0.008192882],"genre_scores_gemma":[0.04766588,0.0004711386,0.9414204,0.0003100239,0.0001236914,0.0004409626,0.002082885,0.0002904763,0.007194489],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01429722,"threshold_uncertainty_score":0.04782897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997980423624899,"score_gpt":0.2493373957629373,"score_spread":0.2293575915266883,"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."}}