{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002743787,0.0004878971,0.0006580356,0.0002185063,0.0002688147,0.0000826573,0.0003606551,0.0006103746,0.0003950591],"category_scores_gemma":[0.0005485911,0.0003900528,0.0001349219,0.0003989224,0.0002838707,0.0001104548,0.0004616687,0.001598352,0.00001599418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003299768,"about_ca_system_score_gemma":0.0003380802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002807823,"about_ca_topic_score_gemma":0.00004906862,"domain_scores_codex":[0.9976071,0.0004680924,0.0002624186,0.001005168,0.0002066784,0.0004505386],"domain_scores_gemma":[0.9976279,0.0008228509,0.000381023,0.000577294,0.0003026893,0.0002882349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001039573,0.0004544712,0.004789255,0.0007271619,0.0003388963,0.0005231044,0.0001423723,0.06039255,0.0005914425,0.9278638,0.001970768,0.001166606],"study_design_scores_gemma":[0.0005562622,0.0001703486,0.001302735,0.0007237386,0.0002940106,0.00002646279,0.00002588597,0.460667,0.0001470998,0.5355577,0.00001580872,0.0005129758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4169115,0.000009675537,0.5821242,0.00007554568,0.0002102451,0.0003677319,0.00002769294,0.000116341,0.0001570691],"genre_scores_gemma":[0.7281109,0.000009303697,0.2708659,0.00003715352,0.0001093269,0.000001631,0.00002075654,0.00004046652,0.0008045561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4002744,"threshold_uncertainty_score":0.9998552,"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."}}