{"id":"W4327936212","doi":"10.5539/ijsp.v12n2p28","title":"Olsavs: A New Algorithm For Model Selection","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lasso (programming language); Ordinary least squares; Shrinkage; Mathematics; Mean squared error; Variance (accounting); Feature selection; Selection (genetic algorithm); Variable (mathematics); Algorithm; Regression; Statistics; Least-squares function approximation; Linear regression; Computer science; Artificial intelligence; Estimator","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005028271,0.001661448,0.001879496,0.002041331,0.0008050995,0.001787438,0.002242143,0.001261806,0.005620778],"category_scores_gemma":[0.0136856,0.001037489,0.002007782,0.001937803,0.0008984661,0.001543811,0.002946519,0.003428207,0.002824952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284436,"about_ca_system_score_gemma":0.002722309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003190364,"about_ca_topic_score_gemma":0.004140453,"domain_scores_codex":[0.995689,0.002457234,0.0002328344,0.0005229659,0.0009599011,0.000138055],"domain_scores_gemma":[0.9959562,0.002526568,0.0003037221,0.0003760567,0.0007411605,0.00009622896],"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":[0.0002406356,0.0000941123,0.002666362,0.0006037927,0.0008516024,0.0002052409,0.0002440817,0.2861435,0.003342907,0.07121233,0.03635693,0.5980386],"study_design_scores_gemma":[0.00008441933,0.0001119426,0.0004740829,0.0001002635,0.00008271028,0.0001561868,0.0000389455,0.9090717,0.001721645,0.05810595,0.03000085,0.00005131686],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007234929,0.0002951375,0.9974321,0.0001247576,0.00008248664,0.00004633869,0.0001499655,0.0007491034,0.0003967171],"genre_scores_gemma":[0.03040049,0.0009218523,0.9609579,0.0004098125,0.0002703766,0.0006958091,0.001911041,0.0007384016,0.003694396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005620778,"threshold_uncertainty_score":0.02659237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1152622829620855,"score_gpt":0.4131536130438684,"score_spread":0.2978913300817829,"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."}}