{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003665643,0.001246796,0.001549052,0.001867074,0.001086144,0.002012674,0.001764555,0.001769304,0.003724647],"category_scores_gemma":[0.01441989,0.0007245084,0.001256795,0.0013845,0.0009462621,0.003086167,0.001210717,0.002290709,0.0007243462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002083505,"about_ca_system_score_gemma":0.001997341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01328405,"about_ca_topic_score_gemma":0.01187408,"domain_scores_codex":[0.9969432,0.001342494,0.0001629115,0.0008252361,0.0004681881,0.0002580441],"domain_scores_gemma":[0.9947987,0.003644137,0.0004109683,0.0002434932,0.0007356821,0.0001670639],"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":[0.0001762945,0.00008378149,0.00383477,0.0001201817,0.00009049725,0.0001659079,0.0001823908,0.8837106,0.001114067,0.02712007,0.001921406,0.08147991],"study_design_scores_gemma":[0.00001122205,0.00001595613,0.0002643307,0.00001774433,0.00001492115,0.00002140596,0.00001502999,0.9815961,0.0002858238,0.01728735,0.0004581214,0.00001205066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01657192,0.0002290315,0.98033,0.0003065987,0.00004786653,0.000114048,0.0001903522,0.0003590363,0.001851261],"genre_scores_gemma":[0.6259817,0.0004371181,0.3685807,0.000289657,0.0001145852,0.0005725642,0.001023566,0.0001243536,0.002875565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01328405,"threshold_uncertainty_score":0.0264135,"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."}}