{"id":"W2949431720","doi":"10.48550/arxiv.1208.3719","title":"Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hyperparameter; Machine learning; Computer science; Hyperparameter optimization; MNIST database; Artificial intelligence; Bayesian optimization; Feature selection; Classifier (UML); Selection (genetic algorithm); Data mining; Artificial neural network; Support vector machine","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":[0.0003396549,0.0002054966,0.0002448499,0.000264722,0.000102948,0.00008660265,0.0005728629,0.0002712861,0.00001565576],"category_scores_gemma":[0.00005950807,0.0002340328,0.00006923897,0.0004751303,0.00007090547,0.0005427208,0.0005101436,0.0003841304,0.00001323349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001030023,"about_ca_system_score_gemma":0.0000841686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000885252,"about_ca_topic_score_gemma":0.000005830294,"domain_scores_codex":[0.9985656,0.0002153505,0.0002307143,0.0007004905,0.00009430156,0.000193618],"domain_scores_gemma":[0.998453,0.00009128548,0.0004550969,0.0007024121,0.0001932307,0.0001049253],"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":[0.00006478423,0.0003454348,0.02471143,0.0002264689,0.0001249533,0.000003888461,0.0004745448,0.7407151,0.0006083568,0.2090213,0.0001581599,0.02354559],"study_design_scores_gemma":[0.000304095,0.00004771032,0.0201466,0.00003048053,0.00006131602,0.00000319051,0.00001973377,0.9774168,0.00008316398,0.001395723,0.0002713805,0.0002198066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04579136,0.00004725651,0.9524153,0.0001380446,0.0002900798,0.0002215993,0.000009890858,0.0001976543,0.00088875],"genre_scores_gemma":[0.9647698,0.0001480142,0.03439193,0.00001985298,0.00004444644,0.000001760617,0.0001594441,0.00001283555,0.0004518699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9189785,"threshold_uncertainty_score":0.954358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07618852931793457,"score_gpt":0.2056777942802454,"score_spread":0.1294892649623109,"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."}}