{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009843935,0.0002744669,0.0003680184,0.000269319,0.000365832,0.0002607068,0.0003065908,0.00009820553,0.00001163134],"category_scores_gemma":[0.00003406481,0.0002684062,0.0001496178,0.0003784458,0.00004200189,0.0001728903,0.00006161301,0.001048259,0.00002516467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991448,"about_ca_system_score_gemma":0.0000319803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002839653,"about_ca_topic_score_gemma":0.00003382834,"domain_scores_codex":[0.9982883,0.0002404044,0.0003570424,0.0003387102,0.000256831,0.0005186675],"domain_scores_gemma":[0.9988167,0.0005739402,0.00003935524,0.0003883679,0.00009579123,0.00008587105],"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":[0.00004928186,0.00003881424,0.00006111455,0.00004117788,0.0004591323,0.000002253308,0.0007962158,0.7387106,0.01878356,0.0008491572,0.00003417326,0.2401745],"study_design_scores_gemma":[0.00131449,0.00009622611,0.00006660271,0.00008236137,0.00008314077,0.000007287337,0.0000620966,0.9947426,0.000883388,0.0003129236,0.002060652,0.0002882431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1349543,0.0345673,0.8272876,0.0003762222,0.0001201853,0.0003310252,0.000001389405,0.0009774657,0.001384569],"genre_scores_gemma":[0.9949315,0.002039453,0.002626788,0.0001675191,0.0000634931,0.00001946625,0.00002325045,0.00006887436,0.00005965443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8599772,"threshold_uncertainty_score":0.9999768,"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."}}