{"id":"W4408791657","doi":"10.1109/access.2025.3554093","title":"Understanding the Role of Diversity in Ensemble-Based AutoML Methods for Classification Tasks","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Ontario Ministry of Research and Innovation; Ministerio de Ciencia e Innovación; Eusko Jaurlaritza; European Commission","keywords":"Computer science; Diversity (politics); Artificial intelligence; Machine learning; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030442,0.0000631675,0.0000991253,0.0001650224,0.0002204809,0.0001119275,0.001170884,0.00004764659,0.000001446251],"category_scores_gemma":[0.0001438727,0.00005025607,0.000037821,0.0005741345,0.00003724671,0.0003098971,0.0001948441,0.0001029448,7.211637e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009750315,"about_ca_system_score_gemma":0.00008336011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001546736,"about_ca_topic_score_gemma":0.00004974333,"domain_scores_codex":[0.9991755,0.0002182089,0.000166126,0.0002274308,0.00009711009,0.0001155768],"domain_scores_gemma":[0.9986355,0.0007027511,0.0001295243,0.000464688,0.00005146682,0.00001612427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004820941,0.0001171833,0.07876293,0.00008947748,0.00002391662,2.45692e-7,0.0008141493,0.003337418,0.01679822,0.6050361,0.0007584975,0.2942136],"study_design_scores_gemma":[0.0003189585,0.00001385119,0.05513343,0.00002533216,0.0000100438,1.071311e-7,0.0001081687,0.8693439,0.009877764,0.06341428,0.001688307,0.0000658257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002124749,0.00005414554,0.9930333,0.002005481,0.0002071465,0.0002100341,0.000002944991,0.00005059125,0.002311631],"genre_scores_gemma":[0.9728937,0.000003016564,0.02685783,0.0001680663,0.00001007567,0.00002315083,0.000006973158,0.00000234617,0.00003488938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9707689,"threshold_uncertainty_score":0.2175813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2221457246106079,"score_gpt":0.4265519126365056,"score_spread":0.2044061880258977,"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."}}