{"id":"W2136524907","doi":"10.1002/wics.1288","title":"Least angle regression for model selection","year":2014,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lasso (programming language); Model selection; Regression diagnostic; Regression analysis; Statistical model; Computer science; Selection (genetic algorithm); Proper linear model; Regression; Exploratory data analysis; Linear regression; Statistics; Graphical model; Artificial intelligence; Machine learning; Mathematics; Polynomial regression","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001391291,0.0008965693,0.003602684,0.0002298598,0.0004582483,0.0001143324,0.0005266697,0.0003635332,0.0001741796],"category_scores_gemma":[0.002168533,0.0006574874,0.0007471116,0.0003363274,0.0001526248,0.00008208696,0.0004557907,0.0005849811,0.0001602455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002935572,"about_ca_system_score_gemma":0.0003097459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.452264e-7,"about_ca_topic_score_gemma":0.000004490101,"domain_scores_codex":[0.9949674,0.0007655284,0.002419839,0.0008848397,0.0004582204,0.0005041774],"domain_scores_gemma":[0.9905749,0.006610345,0.001690607,0.0004265306,0.0004634823,0.0002341356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001280611,0.0001012091,1.937144e-7,0.0283118,0.00006615731,0.000001707144,0.0000517017,0.0001646237,4.184896e-8,0.1722108,0.09937199,0.699707],"study_design_scores_gemma":[0.0001267536,0.0001726025,1.85133e-7,0.01901433,0.0006119575,0.0000373468,0.000003503,0.1625978,2.845665e-8,0.4176337,0.3993751,0.0004267483],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.821494e-8,0.43943,0.556647,0.00001523692,0.0002641559,0.001505613,0.001824186,0.00005694481,0.0002568659],"genre_scores_gemma":[2.197222e-7,0.4756607,0.5217992,0.00003169813,0.0002726681,0.000579662,0.001033459,0.00009031408,0.0005320521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6992802,"threshold_uncertainty_score":0.9995877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2421869945452439,"score_gpt":0.4896456163323069,"score_spread":0.247458621787063,"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."}}