{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002449306,0.001126282,0.001111187,0.002326748,0.0006017662,0.001111928,0.001077208,0.001032462,0.001356189],"category_scores_gemma":[0.006001775,0.0004234754,0.001069212,0.001187097,0.0003531621,0.001354823,0.0006627631,0.001165011,0.0005193207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360411,"about_ca_system_score_gemma":0.001193925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189766,"about_ca_topic_score_gemma":0.01635047,"domain_scores_codex":[0.9989229,0.0004843457,0.00007670468,0.0002359041,0.0001596335,0.0001204276],"domain_scores_gemma":[0.9954329,0.003135947,0.0003604814,0.0002539668,0.0007253487,0.00009133471],"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":[0.00009973338,0.0002358224,0.01147404,0.00003667727,0.00009473695,0.00006619718,0.00006325911,0.9195463,0.0005989484,0.0007207101,0.001090973,0.06597264],"study_design_scores_gemma":[0.000001521539,0.000008407219,0.0002945945,0.000001591239,0.000002937862,0.000002978491,0.000002109767,0.9993715,0.00007047796,0.0001977499,0.00004446965,0.00000170565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4939311,0.0007843902,0.4943468,0.0009691645,0.0001574258,0.0002471835,0.0008320133,0.003821061,0.004910843],"genre_scores_gemma":[0.951079,0.0001162957,0.04616873,0.0001060651,0.00006395254,0.0001438456,0.0006050838,0.00006332216,0.001653768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02189766,"threshold_uncertainty_score":0.04354042,"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."}}