{"id":"W4283018748","doi":"10.3390/jrfm15060269","title":"A Machine Learning Framework towards Bank Telemarketing Prediction","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Exploit; Classifier (UML); Artificial intelligence; Transparency (behavior); Coding (social sciences); Predictive modelling; Field (mathematics); Data mining; Computer security; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002457259,0.0009042856,0.0009723122,0.002092371,0.0005414661,0.002020701,0.001627562,0.001613958,0.001794784],"category_scores_gemma":[0.005164178,0.0004315042,0.0009112356,0.001633679,0.000730393,0.001405164,0.001177955,0.002512618,0.0007388545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132421,"about_ca_system_score_gemma":0.001316466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060208,"about_ca_topic_score_gemma":0.00651602,"domain_scores_codex":[0.9988568,0.000509418,0.00006310533,0.0002733032,0.0002266528,0.00007084123],"domain_scores_gemma":[0.9978998,0.001413259,0.0001589871,0.0001000444,0.0003552809,0.00007266724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006145517,0.0001262721,0.003105605,0.0001072091,0.0001059148,0.0001560226,0.00008157458,0.8572874,0.0006781562,0.02665624,0.00254155,0.1090926],"study_design_scores_gemma":[0.000002218731,0.00001260064,0.0002358829,0.00001350605,0.000004395515,0.00001054767,0.000009612964,0.9869394,0.0001291812,0.01184246,0.0007946883,0.000005515314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01233688,0.001769277,0.9819605,0.00106752,0.000102292,0.00005431725,0.0002510797,0.0005661089,0.001891977],"genre_scores_gemma":[0.6029821,0.00217794,0.3878227,0.0005362227,0.000602046,0.0004075743,0.00102156,0.0001060571,0.004343871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01060208,"threshold_uncertainty_score":0.02108073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006709919720596814,"score_gpt":0.2013547263224188,"score_spread":0.194644806601822,"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."}}