{"id":"W4293791119","doi":"10.3390/app12168270","title":"Intelligent Decision Forest Models for Customer Churn Prediction","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Random forest; Decision tree; Benchmark (surveying); Incentive; Software deployment; Robustness (evolution); Machine learning; Artificial intelligence; Majority rule; Scalability; Data mining; Operations research; Database; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00245335,0.001197976,0.001356433,0.001565243,0.000487122,0.0009775446,0.001474916,0.001214204,0.001603001],"category_scores_gemma":[0.004763815,0.0004562252,0.001471751,0.001626622,0.000338271,0.001188229,0.0005036662,0.001804373,0.0005316693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009702861,"about_ca_system_score_gemma":0.001005397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01910162,"about_ca_topic_score_gemma":0.02104216,"domain_scores_codex":[0.9991805,0.0003274181,0.00005541994,0.0001841449,0.0001240981,0.0001284314],"domain_scores_gemma":[0.996787,0.002348622,0.0002407888,0.00009967372,0.0004456591,0.00007835674],"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":[0.0001104639,0.00009035058,0.004878108,0.00007577883,0.00006043909,0.0000679415,0.00003828889,0.9367439,0.0004185254,0.003154517,0.001732046,0.05262966],"study_design_scores_gemma":[0.000002620211,0.000007588523,0.0001944594,0.000004768626,0.000005693789,0.000005011017,0.000004026695,0.9982612,0.00004326522,0.001343025,0.0001258915,0.000002453592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1434499,0.003056345,0.8461737,0.001052766,0.0001736878,0.0001510604,0.001883486,0.001343171,0.002715916],"genre_scores_gemma":[0.8563179,0.001297094,0.1367107,0.0002686435,0.0001689175,0.0002336195,0.002682502,0.00006546447,0.002255093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01910162,"threshold_uncertainty_score":0.03798085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0455757640572681,"score_gpt":0.2618194326055124,"score_spread":0.2162436685482443,"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."}}