{"id":"W1517808374","doi":"","title":"No Unbiased Estimator of the Variance of K-Fold Cross-Validation","year":2003,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":708,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Generalization; Mathematics; Cross-validation; Variance (accounting); Covariance matrix; Generalization error; Variance decomposition of forecast errors; Minimum-variance unbiased estimator; Eigenvalues and eigenvectors; Covariance; Bias of an estimator; Applied mathematics; Degrees of freedom (physics and chemistry); Consistent estimator; Algorithm; Statistics; Computer science; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03196534,0.001778838,0.003106143,0.002104796,0.001310871,0.003150412,0.002945567,0.002741472,0.002905505],"category_scores_gemma":[0.1086555,0.001098613,0.002487054,0.001954603,0.001801614,0.002959017,0.002475783,0.003801474,0.002238488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267672,"about_ca_system_score_gemma":0.00405357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286524,"about_ca_topic_score_gemma":0.006545553,"domain_scores_codex":[0.9713891,0.01634696,0.002113469,0.004180985,0.005149103,0.0008203926],"domain_scores_gemma":[0.9191212,0.04878993,0.004379653,0.01675363,0.01037332,0.0005822562],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001324093,0.0004961595,0.04067538,0.001862914,0.003714116,0.0002101244,0.0005773444,0.2016774,0.01179883,0.03009474,0.01398629,0.6935827],"study_design_scores_gemma":[0.0001998706,0.0006696553,0.02746064,0.001030317,0.0007422864,0.0006769419,0.0003420087,0.8879801,0.01754491,0.0450766,0.01799477,0.0002817619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01754232,0.001885066,0.9764454,0.0002046445,0.000341275,0.0002101033,0.0003085448,0.001452037,0.001610614],"genre_scores_gemma":[0.4200571,0.001352881,0.5648304,0.0008594759,0.000389677,0.001515053,0.002941568,0.001163166,0.006890527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9680347,"threshold_uncertainty_score":0.1690509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284528476798492,"score_gpt":0.2329795089063419,"score_spread":0.220134224138357,"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."}}