{"id":"W3107245183","doi":"","title":"SELEKSI FITUR FORWARD SELECTION PADA ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI BENIH GANDUM","year":2018,"lang":"ms","type":"article","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Feature selection; Naive Bayes classifier; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Bayes' theorem; Mathematics; Computer science; Statistics; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006969121,0.0004998447,0.0004392742,0.0002609036,0.001180364,0.0007847559,0.001496349,0.0002989069,0.00065173],"category_scores_gemma":[0.0002337097,0.0004709143,0.0001729374,0.001494759,0.0002786075,0.000879563,0.0006036874,0.0006606524,0.003405052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307006,"about_ca_system_score_gemma":0.0003460628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002300986,"about_ca_topic_score_gemma":0.0004606209,"domain_scores_codex":[0.9962887,0.0002195841,0.0005920155,0.001405079,0.0005785743,0.000916029],"domain_scores_gemma":[0.997281,0.0002645982,0.0003228554,0.001203132,0.000559326,0.0003690875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004632822,0.0005117123,0.005868005,0.0001018059,0.0002928066,0.00001010281,0.005027278,0.0001248369,0.00176544,0.04812368,0.3051418,0.6329862],"study_design_scores_gemma":[0.0008626948,0.001318888,0.007271552,0.0001093047,0.0001285341,0.0001265174,0.0003516009,0.3357736,0.007029527,0.002099585,0.6439265,0.001001753],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01601669,0.0002214894,0.9453762,0.005344182,0.002182123,0.0005852295,0.00009906734,0.001146601,0.0290284],"genre_scores_gemma":[0.8036997,0.0001393571,0.1380403,0.001461694,0.002413839,0.00009505583,0.0001736787,0.00007765402,0.0538987],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8073359,"threshold_uncertainty_score":0.9997743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357950067213302,"score_gpt":0.2728697177487945,"score_spread":0.2592902170766614,"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."}}