{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008212256,0.002357312,0.002679831,0.003242584,0.0006673049,0.003095208,0.002484253,0.002412218,0.01117897],"category_scores_gemma":[0.02617456,0.001020808,0.002623957,0.005747739,0.001574704,0.002558533,0.002421005,0.005155254,0.01094663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035231,"about_ca_system_score_gemma":0.002170208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002381078,"about_ca_topic_score_gemma":0.002243782,"domain_scores_codex":[0.9918298,0.005106237,0.0003834125,0.001019624,0.001533744,0.0001270935],"domain_scores_gemma":[0.9883435,0.008807109,0.0007032677,0.001003675,0.001028135,0.0001143064],"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":[0.00007219215,0.00008448363,0.001273762,0.002986448,0.0009316801,0.0002699278,0.000179168,0.07480196,0.001394874,0.3441586,0.05576807,0.5180789],"study_design_scores_gemma":[0.00005511291,0.0001375421,0.001012698,0.001097026,0.0002291123,0.0004567947,0.00009048335,0.2509906,0.001641885,0.5460164,0.198142,0.0001304236],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0003947768,0.02201561,0.9716401,0.001295404,0.0003640029,0.00005062992,0.0004168457,0.0005212724,0.003301327],"genre_scores_gemma":[0.04796893,0.07456421,0.8548162,0.002262887,0.002969546,0.001000705,0.00302282,0.001290846,0.01210388],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01117897,"threshold_uncertainty_score":0.04343104,"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."}}