{"id":"W2042206577","doi":"10.1080/01969722.2015.1012892","title":"Uplift Random Forests","year":2015,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Outcome (game theory); Random forest; Machine learning; Observational study; Artificial intelligence; Action (physics); Simple (philosophy); Range (aeronautics); Data mining; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003853245,0.0001340523,0.0001865018,0.000125411,0.00005922041,0.000401049,0.0001651695,0.00005838245,0.00003204076],"category_scores_gemma":[0.00007112691,0.000118623,0.00005051425,0.000227313,0.00002793193,0.0003581685,0.00006742297,0.00006683893,0.001809054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004091004,"about_ca_system_score_gemma":0.00001877924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009156422,"about_ca_topic_score_gemma":0.0001770747,"domain_scores_codex":[0.9989872,0.00001168921,0.0002699233,0.000183587,0.0003278719,0.0002197343],"domain_scores_gemma":[0.9994068,0.00002307069,0.0001521821,0.0002084219,0.0001804506,0.00002907669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000380113,0.0002973164,0.3504856,0.0006954384,0.000154515,0.00008395314,0.001432347,0.006662507,0.0007531989,0.1108682,0.5176761,0.0105107],"study_design_scores_gemma":[0.007148643,0.00002579842,0.01930409,0.0001387196,0.0001346051,0.00001646281,0.001985108,0.0601461,0.00005465187,0.002875595,0.9075302,0.0006400216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7395355,0.001422443,0.005233957,0.0004670073,0.005765867,0.00100011,0.00000328632,0.0003922145,0.2461796],"genre_scores_gemma":[0.9943489,0.000003815728,0.0000270178,0.0002987931,0.00181887,0.00002982876,0.0000421929,0.00002699992,0.003403594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3898541,"threshold_uncertainty_score":0.9989681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610241145063336,"score_gpt":0.2406608925201367,"score_spread":0.2045584810695033,"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."}}