{"id":"W3081610157","doi":"10.5430/air.v9n1p36","title":"Properly initialized Bayesian Network for decision making leveraging random forest","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian network; Computer science; Random forest; Decision tree; Node (physics); Data mining; Enhanced Data Rates for GSM Evolution; Conditional probability; Inference; Probabilistic logic; Influence diagram; Bayesian probability; Bayesian inference; Product (mathematics); Artificial intelligence; Machine learning; Mathematics; Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002706858,0.0001521295,0.0002906063,0.0002527968,0.0008089732,0.0004888204,0.001994609,0.0001460882,0.00006990864],"category_scores_gemma":[0.002947621,0.0001331197,0.0001410967,0.00223859,0.0002256459,0.000540814,0.0006674156,0.0004973488,0.0002420248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004466044,"about_ca_system_score_gemma":0.0001627265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005080179,"about_ca_topic_score_gemma":0.0001682726,"domain_scores_codex":[0.9970416,0.0002593481,0.0005087147,0.000743465,0.0006064175,0.0008404064],"domain_scores_gemma":[0.996849,0.00187195,0.00007238358,0.0006533318,0.0003881459,0.0001651843],"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":[0.000487994,0.00004358111,0.0004348517,0.00001881526,0.00003537208,0.00004161401,0.0008777654,0.00412196,0.000386622,0.2345974,0.002006314,0.7569478],"study_design_scores_gemma":[0.00007810584,0.000203603,0.00002746195,0.00006884465,0.000007802302,0.000003627937,0.0002658856,0.6634124,0.003411104,0.3292309,0.003128687,0.0001615639],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003734941,0.00017715,0.9884066,0.006443116,0.0001550837,0.0006016743,0.000004943133,0.000220135,0.0002563488],"genre_scores_gemma":[0.8790736,0.00002959661,0.1200899,0.0003643586,0.0003106268,0.0001009948,0.000005974637,0.00001408847,0.00001089733],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8753386,"threshold_uncertainty_score":0.622205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2272639562295495,"score_gpt":0.4239395341372076,"score_spread":0.1966755779076581,"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."}}