{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288117,0.0001155294,0.000180223,0.0004175586,0.0006137697,0.0001373688,0.0001325195,0.00002777533,0.0002423933],"category_scores_gemma":[0.0001778376,0.0001090628,0.00009350591,0.0004370816,0.00001778397,0.0004155158,0.0002608653,0.0005027058,0.000005659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000608251,"about_ca_system_score_gemma":0.00001048266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009078919,"about_ca_topic_score_gemma":0.000006224658,"domain_scores_codex":[0.9988441,0.00004093421,0.0003859684,0.0001376454,0.0004235255,0.0001677813],"domain_scores_gemma":[0.9992789,0.00003471077,0.0005383787,0.00006784281,0.00006550852,0.00001467876],"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":[0.0004461732,0.0001455709,0.1221334,0.000153416,0.00004329081,0.0001064987,0.0005353422,0.002312741,0.00001739677,0.01027692,0.002346304,0.8614829],"study_design_scores_gemma":[0.001263981,0.0001092439,0.2200553,0.00007722421,0.0002515593,0.00002495929,0.001911307,0.004461573,0.000002762807,0.008194633,0.7634487,0.0001988051],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8911828,0.002452114,0.08641455,0.0007916655,0.003471285,0.0005038469,0.00001915419,0.0001077972,0.01505683],"genre_scores_gemma":[0.9962043,0.0007909587,0.001217761,0.0004994429,0.001088459,0.000009784731,0.000009590416,0.00001525251,0.0001644445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8612841,"threshold_uncertainty_score":0.4720683,"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."}}