{"id":"W2561265960","doi":"10.1080/01621459.2017.1407775","title":"Censoring Unbiased Regression Trees and Ensembles","year":2018,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Cancer Institute","keywords":"Censoring (clinical trials); Random forest; Regression; Statistics; Inference; Mathematics; Computer science; Mean squared error; Regression analysis; Artificial intelligence; Machine learning","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":[0.004812972,0.0007676299,0.001271285,0.001611683,0.0006255902,0.001268281,0.001373761,0.001087952,0.001657293],"category_scores_gemma":[0.01688759,0.000395,0.0009631017,0.001803674,0.0008929431,0.002180852,0.001523075,0.00213296,0.000637317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005729484,"about_ca_system_score_gemma":0.0009568823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001580047,"about_ca_topic_score_gemma":0.002122201,"domain_scores_codex":[0.9967794,0.001569711,0.0001337779,0.0004235225,0.0009319965,0.0001616487],"domain_scores_gemma":[0.9936805,0.003783206,0.0005607635,0.0009513533,0.0008897958,0.0001343631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000662221,0.00004308143,0.003207953,0.0001295278,0.0001713449,0.0001068468,0.0001663243,0.4966471,0.001690024,0.2830397,0.004064482,0.2106672],"study_design_scores_gemma":[0.00000929176,0.00002792342,0.0006282679,0.0000387498,0.00002512661,0.00007711737,0.00001957901,0.8402878,0.0009353048,0.1532879,0.004642273,0.00002073848],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003066291,0.0003673116,0.9955162,0.00009550375,0.00002580546,0.00001011839,0.00004670711,0.00015899,0.0007129211],"genre_scores_gemma":[0.3281512,0.002070539,0.6639069,0.0004629314,0.0005537632,0.0002580594,0.0009167427,0.0002369939,0.003442901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004812972,"threshold_uncertainty_score":0.02545375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0496418759659346,"score_gpt":0.3841708336032706,"score_spread":0.334528957637336,"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."}}