{"id":"W4399663950","doi":"10.12928/telkomnika.v22i4.25847","title":"Multi objective hyperparameter tuning via random search on deep learning models","year":2024,"lang":"en","type":"article","venue":"TELKOMNIKA (Telecommunication Computing Electronics and Control)","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknologi MARA","keywords":"Hyperparameter; Random search; Computer science; Artificial intelligence; Machine learning; Hyperparameter optimization; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005541612,0.002078093,0.001472792,0.001579421,0.0003994148,0.001190959,0.001574002,0.001458764,0.001583605],"category_scores_gemma":[0.01737233,0.0005193684,0.0008291657,0.0008629904,0.0009577606,0.00173349,0.001158376,0.001635114,0.0004642579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00106598,"about_ca_system_score_gemma":0.001248202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490476,"about_ca_topic_score_gemma":0.00489035,"domain_scores_codex":[0.9979067,0.00137711,0.00009329745,0.0002819207,0.0002201133,0.0001210176],"domain_scores_gemma":[0.9903386,0.008103206,0.00048944,0.0004513041,0.0004910867,0.0001262533],"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":[0.0001832237,0.000115136,0.001572491,0.0001470092,0.00010949,0.00006824132,0.00004582117,0.9525396,0.0007365274,0.003096803,0.001298498,0.04008725],"study_design_scores_gemma":[0.00003124233,0.00007211461,0.0001342443,0.00002803029,0.00001600324,0.00001763519,0.00001761507,0.9964992,0.0004512944,0.002429554,0.0002960006,0.000006992192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.23552,0.006478783,0.7448353,0.001084113,0.0001661115,0.0003343263,0.0002412173,0.002955373,0.008384812],"genre_scores_gemma":[0.8487968,0.000598714,0.1475125,0.0005403027,0.00005197584,0.0002758659,0.0003804743,0.0003254929,0.001517877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005541612,"threshold_uncertainty_score":0.02930719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891612887932724,"score_gpt":0.2349294254175796,"score_spread":0.2160132965382523,"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."}}