{"id":"W2916199673","doi":"10.1177/0962280219829885","title":"Prediction intervals with random forests","year":2019,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Random forest; Computer science; Covariate; Prediction interval; Calibration; Tree (set theory); Data mining; Regression; Interval (graph theory); Parametric statistics; Cart; Statistics; Machine learning; Mathematics","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":[0.02037041,0.001425278,0.001894313,0.002784066,0.0006220746,0.001868318,0.002557801,0.001461033,0.003310496],"category_scores_gemma":[0.06609956,0.0006662472,0.001861418,0.001916902,0.0008224341,0.002779534,0.001850092,0.002704351,0.001195007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006598293,"about_ca_system_score_gemma":0.0009896547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002121835,"about_ca_topic_score_gemma":0.001555783,"domain_scores_codex":[0.9872742,0.008862781,0.0005746597,0.001329213,0.001604891,0.0003542848],"domain_scores_gemma":[0.9458947,0.0450579,0.002356201,0.003351605,0.002960076,0.0003795714],"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":[0.0007835348,0.0001347794,0.005721837,0.000456553,0.0003989645,0.0001836064,0.0002086006,0.6003473,0.000840914,0.0333064,0.005011314,0.3526062],"study_design_scores_gemma":[0.00005031433,0.00009215408,0.0006609434,0.00008048899,0.00005782699,0.00007456327,0.00002249619,0.9663435,0.0009104668,0.03006293,0.001611924,0.00003241723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009877689,0.001045613,0.9867678,0.0001202323,0.0001051555,0.0001099291,0.0001746313,0.000914113,0.0008847719],"genre_scores_gemma":[0.3905929,0.0009556232,0.6048744,0.000194559,0.0002921378,0.000538856,0.001117679,0.0003242918,0.001109518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02037041,"threshold_uncertainty_score":0.1077303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1173314666053891,"score_gpt":0.5295953286551724,"score_spread":0.4122638620497834,"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."}}