{"id":"W2112364454","doi":"10.25080/majora-14bd3278-006","title":"Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Python in Science Conferences","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":298,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Hyperparameter; Computer science; Artificial intelligence; Preprocessor; Machine learning; Benchmarking; Classifier (UML); Support vector machine; Pattern recognition (psychology); Data mining; Deep learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005664534,0.005132728,0.002487261,0.003883565,0.001080408,0.003849433,0.007976645,0.004311373,0.05109996],"category_scores_gemma":[0.03129011,0.002847017,0.003213817,0.002771943,0.001128767,0.005325556,0.005578367,0.007904643,0.05881105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720243,"about_ca_system_score_gemma":0.002878956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002540538,"about_ca_topic_score_gemma":0.005510845,"domain_scores_codex":[0.9963265,0.001103962,0.0004969759,0.0008148384,0.000952078,0.0003056406],"domain_scores_gemma":[0.9911089,0.004711919,0.0004215411,0.002161069,0.001192277,0.000404273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001203991,0.0008000963,0.006309112,0.002255243,0.0007873792,0.0004421727,0.0005048895,0.0388262,0.007114233,0.009392462,0.4976523,0.434712],"study_design_scores_gemma":[0.001299699,0.0002835487,0.003089009,0.0005765711,0.0002160005,0.0004493054,0.0002238714,0.7489264,0.0359644,0.07540168,0.1331803,0.0003892129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.005988665,0.0006245574,0.3039908,0.0002716188,0.0003452025,0.0004292746,0.004883424,0.6777152,0.005751205],"genre_scores_gemma":[0.09153431,0.0007119813,0.6414686,0.001601793,0.00026677,0.005479333,0.03079169,0.2163929,0.01175252],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.05109996,"threshold_uncertainty_score":0.1709464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458341355120219,"score_gpt":0.2767362059639611,"score_spread":0.252152792412759,"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."}}