{"id":"W4286518196","doi":"10.18280/ria.360304","title":"Churn Prediction Model Improvement Using Automated Machine Learning with Social Network Parameters","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Centrality; Computer science; Social network (sociolinguistics); Revenue; Competition (biology); Data science; Machine learning; Artificial intelligence; Social network analysis; Revenue model; Social media; World Wide Web; Business","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":[0.0004282294,0.0001686248,0.0001669604,0.0001562309,0.001239404,0.0001722522,0.000168774,0.00003287278,0.0002960296],"category_scores_gemma":[0.00001316011,0.0001713964,0.00007160038,0.0007561633,0.00004213007,0.0004200668,0.0001710041,0.0002794942,0.0000474068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535099,"about_ca_system_score_gemma":0.0000235873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000301635,"about_ca_topic_score_gemma":0.00002735723,"domain_scores_codex":[0.9986458,0.00002256001,0.0003490146,0.0003287087,0.0002998344,0.0003540446],"domain_scores_gemma":[0.9994907,0.00001897869,0.0002689124,0.0001354561,0.00007183856,0.0000140641],"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":[0.00008263157,0.00008072629,0.002267174,0.00004506029,0.00002493616,0.000005269019,0.0003955287,0.9863371,0.003259772,0.0007031183,0.0004898712,0.006308794],"study_design_scores_gemma":[0.0001080614,0.00005322062,0.0000650837,0.00001800898,0.0000579218,0.000005379909,0.001546739,0.9952749,0.0004292546,0.0003836259,0.001860498,0.0001973155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8826431,0.00006132401,0.113283,0.0003743979,0.0004672545,0.0005351042,0.000009731873,0.0006820893,0.00194404],"genre_scores_gemma":[0.9976562,0.000004399007,0.0008390172,0.0004850226,0.0003140533,0.00005825088,0.0001300792,0.0000363572,0.0004766457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1150131,"threshold_uncertainty_score":0.9532621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04086890788483465,"score_gpt":0.2510407057785353,"score_spread":0.2101717978937007,"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."}}