{"id":"W3132426513","doi":"10.1371/journal.pone.0246159","title":"HDSI: High dimensional selection with interactions algorithm on feature selection and testing","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Lasso (programming language); Feature selection; Leverage (statistics); Statistical hypothesis testing; Computer science; Selection (genetic algorithm); Statistical model; Feature (linguistics); Artificial intelligence; Statistical inference; Model selection; Machine learning; Algorithm; Pattern recognition (psychology); Data mining; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004554048,0.00009290716,0.00009898759,0.00007562626,0.0002365758,0.0001136533,0.00005057974,0.00004579172,0.00002870238],"category_scores_gemma":[0.00005628923,0.00008019623,0.00001216501,0.0004751798,0.000009510609,0.000377895,0.0000454006,0.0002374014,0.00002971058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003659853,"about_ca_system_score_gemma":0.00004944355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002195281,"about_ca_topic_score_gemma":0.00003189809,"domain_scores_codex":[0.9991826,0.0000465089,0.00007580878,0.0003102493,0.0002515853,0.0001332188],"domain_scores_gemma":[0.9994023,0.0001094427,0.00004928217,0.00009502441,0.0002853413,0.00005864355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007645636,0.003217827,0.004672481,0.00008773767,0.0003255145,0.00005725398,0.0003756467,0.0005629271,0.6901027,0.0008246502,0.004729297,0.2949675],"study_design_scores_gemma":[0.0006228493,0.0005382032,0.01048501,0.0007617333,0.00006071438,0.0002156052,0.00002838795,0.436359,0.5495653,0.0008504586,0.0002231461,0.0002895604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540942,0.00003149219,0.04194292,0.002791504,0.0001287107,0.0001407448,0.000004781754,0.000280467,0.0005851216],"genre_scores_gemma":[0.5198549,0.000009304273,0.478053,0.0004909919,0.0001816432,0.00003030543,0.00002380599,0.00001165901,0.001344411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.43611,"threshold_uncertainty_score":0.3270307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050915829329847,"score_gpt":0.2177722235576707,"score_spread":0.1872630652643722,"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."}}