{"id":"W2788221235","doi":"","title":"BOOTSTRAPPING AND WEIGHTED INFORMATION GAIN IN SUPPORT VECTOR MACHINE FOR CUSTOMER LOYALTY PREDICTION","year":2018,"lang":"en","type":"article","venue":"The Journal of Internet Banking and Commerce","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bootstrapping (finance); Computer science; Support vector machine; Hyperparameter optimization; Loyalty; Artificial intelligence; Machine learning; Selection (genetic algorithm); Data mining; Loyalty business model; Value (mathematics); Feature selection; Econometrics; Mathematics; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007416224,0.00007794082,0.0001122954,0.000210483,0.0000779572,0.0001277981,0.00008750811,0.0000294002,0.00005742195],"category_scores_gemma":[0.00002670097,0.00005396761,0.00002447817,0.0001092773,0.0000467175,0.0009027938,0.0000397942,0.0001207129,0.000006909796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000206579,"about_ca_system_score_gemma":0.000008055476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001955625,"about_ca_topic_score_gemma":0.00009747857,"domain_scores_codex":[0.9994025,0.00001606912,0.000324446,0.0000441429,0.0001097485,0.0001031389],"domain_scores_gemma":[0.9994907,0.00005025698,0.0002846388,0.00004784271,0.0001180953,0.000008478134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006290423,0.0003818784,0.3294815,0.001435449,0.0005088536,0.00001062482,0.02860797,0.0002144525,0.006174604,0.0179072,0.1226524,0.4863347],"study_design_scores_gemma":[0.009848067,0.0008016207,0.4276582,0.001107719,0.0004620951,0.0003087648,0.004487905,0.2915663,0.0012821,0.004443348,0.2573643,0.0006695214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845021,0.00007352267,0.01091869,0.001248134,0.0003216834,0.0001515358,0.000003098912,0.00001383923,0.002767401],"genre_scores_gemma":[0.9977962,0.00003688415,0.00004323146,0.001670715,0.0003676874,0.000001510229,0.00001141234,0.000005771563,0.00006658982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4856651,"threshold_uncertainty_score":0.2200735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533521938632458,"score_gpt":0.2382625188568314,"score_spread":0.2229272994705068,"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."}}