{"id":"W1552872738","doi":"10.48550/arxiv.1308.4690","title":"High-dimensional Feature Selection Using Hierarchical Bayesian Logistic Regression with Heavy-tailed Priors","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Computer science; Bayesian probability; Feature selection; Logistic regression; Machine learning; Data mining; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00928082,0.001143111,0.00188556,0.001866489,0.0008837857,0.001550308,0.002330193,0.001286815,0.001402891],"category_scores_gemma":[0.02024302,0.0007193503,0.001886716,0.00214565,0.001023922,0.00163047,0.00173526,0.002272775,0.000797613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026479,"about_ca_system_score_gemma":0.001531902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00772185,"about_ca_topic_score_gemma":0.007991619,"domain_scores_codex":[0.9946187,0.003640797,0.0001817023,0.0006599855,0.0006544585,0.0002444234],"domain_scores_gemma":[0.9850467,0.01216056,0.0007731491,0.000917371,0.0008619711,0.0002401759],"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":[0.0007820792,0.0003758855,0.0156934,0.0002636577,0.0006087096,0.000410965,0.0002773227,0.6504366,0.005798518,0.01714031,0.01009294,0.2981196],"study_design_scores_gemma":[0.0000525962,0.00002927863,0.001027056,0.000009428405,0.00001835789,0.00003158317,0.00001295488,0.9875681,0.0004806105,0.01046119,0.0002924714,0.00001638082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02637576,0.0003626345,0.9714832,0.0004259831,0.00001794212,0.00006193027,0.0001775909,0.0007783863,0.0003165815],"genre_scores_gemma":[0.5996507,0.0004137419,0.395376,0.0004316879,0.0001563278,0.0003486893,0.001669526,0.0001799497,0.001773299],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00928082,"threshold_uncertainty_score":0.04908222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1391872289973469,"score_gpt":0.2739638953736737,"score_spread":0.1347766663763268,"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."}}