{"id":"W1591760165","doi":"10.1023/a:1013943418833","title":"Model Selection for Small Sample Regression","year":2002,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Overfitting; Estimator; Generalization; Model selection; Generalization error; Mathematics; Covariance matrix; Artificial intelligence; Selection (genetic algorithm); Linear regression; Applied mathematics; Regression; Sample size determination; Computer science; Statistics; Artificial neural network","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":[0.008449601,0.001493546,0.003042012,0.001454532,0.0008623858,0.00140586,0.002529782,0.001698075,0.005845841],"category_scores_gemma":[0.03571507,0.001370699,0.001781841,0.001525926,0.0009505574,0.002100894,0.001486319,0.003554572,0.002327991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007939163,"about_ca_system_score_gemma":0.001282398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002953499,"about_ca_topic_score_gemma":0.003136944,"domain_scores_codex":[0.9956636,0.003182198,0.0001376585,0.0004455331,0.0004372896,0.0001337739],"domain_scores_gemma":[0.9749265,0.02188084,0.0005185838,0.00148584,0.0009662104,0.0002219607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007737982,0.000300501,0.00316704,0.000709627,0.0009427129,0.0004681339,0.0001238167,0.5831216,0.003422794,0.07582723,0.02261609,0.3085266],"study_design_scores_gemma":[0.00004056881,0.00003322147,0.0002507144,0.00001253763,0.00003964151,0.00002751237,0.000006297491,0.9718876,0.0004601916,0.02605825,0.001173906,0.000009518382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002822021,0.0006377593,0.9950647,0.0002710991,0.00007703193,0.00004508892,0.0001210723,0.0005798171,0.0003814046],"genre_scores_gemma":[0.3419072,0.002696171,0.6346595,0.0006166431,0.0009495785,0.001506626,0.003154228,0.001440692,0.01306935],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008449601,"threshold_uncertainty_score":0.04468626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05444036630139729,"score_gpt":0.265644620019226,"score_spread":0.2112042537178287,"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."}}