{"id":"W3133891263","doi":"10.48550/arxiv.2102.13347","title":"MDA for random forests: inconsistency, and a practical solution via the\\n Sobol-MDA","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Sobol sequence; Random forest; Econometrics; Mathematics; Computer science; Environmental science; Statistics; Artificial intelligence; Monte Carlo method","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":[0.002306085,0.0002971449,0.0005282442,0.0001922664,0.0003557556,0.0003120799,0.0006116952,0.0003636679,0.00005500056],"category_scores_gemma":[0.004492838,0.0002245151,0.0003202031,0.0004524374,0.0003600088,0.0002839442,0.0010077,0.0005284046,0.00002118634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001355355,"about_ca_system_score_gemma":0.0004390917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001048267,"about_ca_topic_score_gemma":0.0002632643,"domain_scores_codex":[0.9975242,0.0003343926,0.0004250909,0.00110055,0.0002603911,0.0003553842],"domain_scores_gemma":[0.9936107,0.004345197,0.0003219199,0.001024953,0.0005183635,0.0001788801],"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":[0.001866258,0.0005081125,0.007511749,0.0003942437,0.0007680309,0.0008580649,0.001363357,0.7865146,0.000186732,0.1802117,0.01428856,0.005528564],"study_design_scores_gemma":[0.001459775,0.00006181579,0.001418028,0.00008103204,0.0002554005,0.00004639737,0.000556403,0.7928249,0.00002138677,0.2013095,0.001591946,0.0003733916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04508124,0.0002629572,0.9518793,0.0006857266,0.0007681664,0.0007303252,0.0000242003,0.00007063506,0.0004974976],"genre_scores_gemma":[0.9929182,0.0001112053,0.005352294,0.00008015412,0.0001252283,0.000008356724,0.00001561492,0.00001946841,0.001369511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9478369,"threshold_uncertainty_score":0.9155462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2051930080384089,"score_gpt":0.2670998338962951,"score_spread":0.06190682585788618,"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."}}