{"id":"W4386814135","doi":"10.6000/1929-6029.2023.12.13","title":"Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Faculty of Medicine Siriraj Hospital, Mahidol University","keywords":"Multicollinearity; Lasso (programming language); Feature selection; Estimator; Computer science; Mean squared error; Mathematics; Penalty method; Artificial intelligence; Statistics; Pattern recognition (psychology); Algorithm; Machine learning; Regression analysis; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009869124,0.0001158506,0.0002981348,0.0004990272,0.00007575397,0.00005755225,0.001150359,0.000126131,0.0002771929],"category_scores_gemma":[0.09819599,0.00008379757,0.00002656298,0.0003890392,0.0003560939,0.0000912336,0.0002490006,0.001080637,0.00003244895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002148934,"about_ca_system_score_gemma":0.0007726603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005484308,"about_ca_topic_score_gemma":0.0001068107,"domain_scores_codex":[0.9939228,0.0005033915,0.0007667388,0.0002857424,0.004185043,0.0003363092],"domain_scores_gemma":[0.9644507,0.03219886,0.0002557737,0.0002931712,0.002548089,0.0002534674],"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":[0.001855562,0.0005318752,0.0003971454,0.00003624858,0.0001253916,0.0007656965,0.00008577756,0.00006672381,0.00008216661,0.8717659,0.04100673,0.08328075],"study_design_scores_gemma":[0.002444237,0.0009470049,0.01157366,0.0005874865,0.00001645213,0.00003494707,0.0002027012,0.4226301,0.00006765207,0.5599412,0.001436759,0.0001177854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02805365,0.00001136298,0.962123,0.006361096,0.0006603225,0.0004047885,0.001964853,0.00001787231,0.0004030663],"genre_scores_gemma":[0.353027,0.0001035383,0.6459994,0.0001152369,0.0004180873,0.00002442013,0.0001316132,0.00002530491,0.0001554145],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4225634,"threshold_uncertainty_score":0.9094003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.53683462234748,"score_gpt":0.5753388564817199,"score_spread":0.03850423413423987,"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."}}