{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006926655,0.00009039889,0.00008847281,0.0002742289,0.001022872,0.0002112974,0.0002831331,0.00001760538,0.0002296339],"category_scores_gemma":[0.00001186297,0.00008027592,0.00004839762,0.0006751333,0.00007203475,0.00062194,0.0001689603,0.00006386911,0.00007772094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004764051,"about_ca_system_score_gemma":0.00002651125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000548176,"about_ca_topic_score_gemma":0.00004699154,"domain_scores_codex":[0.9988336,0.000002927788,0.0001935022,0.0002918061,0.0004658695,0.0002123244],"domain_scores_gemma":[0.9996932,0.00005306602,0.0001101128,0.00009611102,0.00003794772,0.000009598077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001867133,0.0001894862,0.00435573,0.00004884495,0.00001524444,8.701247e-7,0.0004056558,0.2868432,0.002721929,0.5626956,0.0291732,0.1133636],"study_design_scores_gemma":[0.0009080588,0.00005450529,0.002182671,0.00001032717,0.00005023233,0.000002378142,0.005301347,0.6558495,0.0003299247,0.1590927,0.175866,0.0003523251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.696148,0.00009265426,0.1705399,0.000700216,0.002124286,0.001600548,0.00001947929,0.000282139,0.1284927],"genre_scores_gemma":[0.9973435,0.00000388637,0.0007994813,0.001073693,0.0003162322,0.0002431974,0.00003997138,0.000009392304,0.0001706661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4036028,"threshold_uncertainty_score":0.7867209,"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."}}