{"id":"W2135420268","doi":"10.1109/grc.2007.40","title":"Naïve Bayes Text Classifier","year":2007,"lang":"en","type":"article","venue":"2007 IEEE International Conference on Granular Computing (GRC 2007)","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Naive Bayes classifier; Computer science; Bayes' theorem; Support vector machine; Artificial intelligence; Machine learning; Detector; Classifier (UML); Bayesian probability; Information retrieval; Data mining","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":[0.001884984,0.0007581442,0.001385165,0.002727088,0.001107136,0.003427191,0.001705274,0.002022303,0.02024359],"category_scores_gemma":[0.007755195,0.0004214859,0.0006724125,0.001972695,0.0008148871,0.00337867,0.0008936563,0.001083396,0.01736278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034941,"about_ca_system_score_gemma":0.001144075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003513016,"about_ca_topic_score_gemma":0.003807526,"domain_scores_codex":[0.9978637,0.0002779877,0.0001886377,0.0003707986,0.001138551,0.0001603781],"domain_scores_gemma":[0.9975968,0.0008626677,0.0001709323,0.0003415672,0.0009563059,0.00007176775],"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":[0.0002712108,0.000174459,0.004665375,0.0003580932,0.00005578523,0.0002440352,0.0001362161,0.008869835,0.01178922,0.04649443,0.03920837,0.887733],"study_design_scores_gemma":[0.00008832236,0.0001649514,0.00453983,0.0003481882,0.00009092948,0.001498584,0.0002313278,0.6756578,0.04065933,0.1106354,0.1659673,0.0001179334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01555,0.001935342,0.9220532,0.001211342,0.0007366374,0.0004113109,0.00190241,0.007991184,0.04820859],"genre_scores_gemma":[0.2116734,0.001702125,0.7000576,0.0008921443,0.0005368983,0.0005492851,0.003275638,0.000566436,0.08074653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02024359,"threshold_uncertainty_score":0.06772155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07050096873332103,"score_gpt":0.3294173998244482,"score_spread":0.2589164310911272,"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."}}