{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000802494,0.00006622533,0.00006225872,0.00003053182,0.0003078413,0.0000659939,0.0001987431,0.00002656543,0.00001632327],"category_scores_gemma":[0.00003472656,0.00005516237,0.00003771874,0.0001689922,0.000005303495,0.00009603307,0.00005823122,0.0001262059,0.00001012193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001164958,"about_ca_system_score_gemma":0.000002787867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002843752,"about_ca_topic_score_gemma":0.00002426404,"domain_scores_codex":[0.9994864,0.00001940257,0.00008488158,0.0002003987,0.00005841202,0.0001505337],"domain_scores_gemma":[0.9996732,0.00009375621,0.00004927771,0.0001213437,0.00002502894,0.0000373411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003386408,0.00006558645,0.003283631,0.00001170497,0.000005596336,2.68561e-7,0.0002758704,0.5787678,0.003406069,0.03415692,0.003978367,0.3760448],"study_design_scores_gemma":[0.0001075466,0.00003228708,0.00005169276,0.000005646386,0.000001792705,0.000002545857,8.781411e-7,0.9702407,0.0001834626,0.003244448,0.0260605,0.00006845943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008386652,0.00008692452,0.989285,0.001413447,0.00003657787,0.0001030151,9.552846e-7,0.0002023816,0.0004850313],"genre_scores_gemma":[0.7670095,0.00001968189,0.2300632,0.0002269854,0.00008882468,0.00004314207,0.00000657336,0.000009316187,0.002532744],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7592218,"threshold_uncertainty_score":0.2367697,"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."}}