{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034249,0.001083243,0.001428457,0.001049345,0.0006386729,0.001256497,0.0009429003,0.0007690188,0.004333448],"category_scores_gemma":[0.003016531,0.0003411464,0.001134932,0.0006895462,0.0003365642,0.0008521821,0.0003308008,0.001032814,0.00160941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000696525,"about_ca_system_score_gemma":0.001329376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019356,"about_ca_topic_score_gemma":0.008205757,"domain_scores_codex":[0.9988776,0.0003059449,0.0001147838,0.0002238723,0.0003585683,0.0001192318],"domain_scores_gemma":[0.9988006,0.0005758245,0.00005138943,0.00005162629,0.000490777,0.00002966555],"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":[0.0007048254,0.0002750773,0.007756697,0.0003180049,0.000303902,0.0002711967,0.0001490054,0.07664435,0.01179599,0.001802497,0.007084669,0.8928939],"study_design_scores_gemma":[0.0000979046,0.000300502,0.005105386,0.0001281538,0.0002747479,0.0005143884,0.0001280224,0.9754357,0.01047827,0.003145973,0.00433626,0.00005466998],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1887596,0.007010147,0.7846207,0.002426215,0.0009974741,0.0003921614,0.0005349043,0.003131108,0.01212771],"genre_scores_gemma":[0.6987247,0.001813851,0.283097,0.0006366645,0.0003628427,0.0002544764,0.0008343511,0.0001738408,0.01410234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01019356,"threshold_uncertainty_score":0.02026844,"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."}}