{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004414388,0.001196492,0.001808006,0.001850842,0.0007947815,0.00112704,0.001869692,0.001485988,0.003628656],"category_scores_gemma":[0.008143941,0.0006309899,0.001961459,0.001460611,0.0004842219,0.00117387,0.0009396457,0.001629876,0.001983957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578146,"about_ca_system_score_gemma":0.001082659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00610239,"about_ca_topic_score_gemma":0.01439531,"domain_scores_codex":[0.9979335,0.001005649,0.00008883457,0.0004422784,0.0003465058,0.0001831651],"domain_scores_gemma":[0.9953402,0.003141352,0.0002960118,0.0006041676,0.0005094748,0.0001087453],"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":[0.0003845485,0.000261654,0.006204402,0.0002685556,0.0003629081,0.0002790808,0.00008958171,0.6482788,0.001407141,0.01730286,0.02595159,0.2992089],"study_design_scores_gemma":[0.0000332743,0.00003697439,0.0004381509,0.00002057521,0.0000288617,0.00005703232,0.00001250382,0.9818657,0.0004069254,0.01432132,0.002767178,0.0000114505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03014512,0.001204479,0.9578324,0.0004838143,0.0002645636,0.0003381307,0.002294606,0.004205907,0.003230958],"genre_scores_gemma":[0.446494,0.0007561184,0.5382137,0.0006334782,0.0004449862,0.0006482114,0.007076247,0.0004640592,0.005269241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00610239,"threshold_uncertainty_score":0.02334583,"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."}}