{"id":"W2030623877","doi":"10.1142/s0218001405003983","title":"EXPLORING CONDITIONS FOR THE OPTIMALITY OF NAÏVE BAYES","year":2005,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Bayes' theorem; Conditional independence; Naive Bayes classifier; Independence (probability theory); Artificial intelligence; Machine learning; Class (philosophy); Bayesian probability; Bayesian programming; Computer science; Distribution (mathematics); Mathematics; Gaussian; Bayesian network; Conditional probability distribution; Conditional probability; Prior probability; Algorithm; Bayes factor; Econometrics; Statistics; Support vector machine; Physics","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":[0.0291092,0.001366394,0.002811994,0.003243696,0.002132519,0.003935935,0.002931621,0.003510461,0.005464826],"category_scores_gemma":[0.1618975,0.001862018,0.001897153,0.001994245,0.007674451,0.009186916,0.003657842,0.005857447,0.001479309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004150871,"about_ca_system_score_gemma":0.005693606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003769928,"about_ca_topic_score_gemma":0.003118847,"domain_scores_codex":[0.9733888,0.01099931,0.001899284,0.004217738,0.00710908,0.002385722],"domain_scores_gemma":[0.7512526,0.2193555,0.006517357,0.007636661,0.0131825,0.002055358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003886456,0.0002166397,0.008440869,0.0004659899,0.0001578062,0.0003060556,0.0006720819,0.1409635,0.002213186,0.7839621,0.006183723,0.05602959],"study_design_scores_gemma":[0.00006614263,0.00007191144,0.0007251755,0.00009817728,0.00002712299,0.00009382178,0.00005846231,0.3108769,0.0009217499,0.6855325,0.001499639,0.00002840064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0496323,0.00144273,0.9238169,0.005874965,0.000189051,0.0002358363,0.0004068831,0.0004600568,0.01794124],"genre_scores_gemma":[0.6105101,0.001730402,0.3794793,0.002242906,0.0009749425,0.0004473511,0.0009462271,0.0003672595,0.003301617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0291092,"threshold_uncertainty_score":0.153946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3707631980474821,"score_gpt":0.367587719428236,"score_spread":0.003175478619246108,"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."}}