{"id":"W1509534053","doi":"10.7939/r36d5ph6n","title":"Explaining Naive Bayes Classifications","year":2003,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Naive Bayes classifier; Computer science; Machine learning; Classifier (UML); Artificial intelligence; Training set; Bayes' theorem; Bayes classifier; Data mining; Bayesian probability; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001567888,0.00006394474,0.00005826922,0.00004213553,0.0001226525,0.0001136398,0.0003306834,0.00003166051,0.00006551443],"category_scores_gemma":[0.00005600764,0.00005567575,0.00002526155,0.0002056265,0.00001922063,0.0002556873,0.00003013917,0.00007372886,0.0002065242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001156083,"about_ca_system_score_gemma":0.00006234913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005422313,"about_ca_topic_score_gemma":0.000001902185,"domain_scores_codex":[0.99937,0.0000418066,0.0001098133,0.0002181347,0.0001015286,0.0001586614],"domain_scores_gemma":[0.9994559,0.00005851624,0.00002476976,0.000338092,0.00005359318,0.00006917178],"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":[1.014199e-7,0.00001167904,0.0001509691,6.640268e-7,0.00000214683,8.620636e-7,0.0002105712,0.00004006198,0.0003718226,0.9926718,0.0009660108,0.0055733],"study_design_scores_gemma":[0.0005432676,0.00014843,0.002214502,0.00004988314,0.00001102461,0.00007808894,0.0009863277,0.5799409,0.02967465,0.3335415,0.05181569,0.0009958161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001135342,0.00003627588,0.8647421,0.0005141227,0.0001114728,0.0000261087,2.020258e-7,0.000186268,0.1332481],"genre_scores_gemma":[0.8049917,0.000006436282,0.1935339,0.0003381346,0.000009009738,0.000008947282,3.66292e-7,0.000002678155,0.001108901],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8038563,"threshold_uncertainty_score":0.265452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04605557111486784,"score_gpt":0.2702789424882792,"score_spread":0.2242233713734113,"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."}}