{"id":"W4402169893","doi":"10.32920/26883781","title":"Stochastic-Based Hyperparameter Selection and Learnability Analysis in Supervised and Unsupervised Learning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Learnability; Hyperparameter; Machine learning; Artificial intelligence; Computer science; Selection (genetic algorithm); Statistical learning; Unsupervised learning; Supervised learning; Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001083172,0.0003066535,0.0004714995,0.0009600965,0.0001200372,0.0007589824,0.0003613117,0.0002684217,0.00003493175],"category_scores_gemma":[0.000322048,0.0002808107,0.0001112559,0.001187149,0.00006324456,0.0001483907,0.001020484,0.001506213,0.00001221835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008890963,"about_ca_system_score_gemma":0.0001462243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369052,"about_ca_topic_score_gemma":0.0005483264,"domain_scores_codex":[0.9972329,0.0004641347,0.0004028311,0.001363402,0.0002730764,0.0002636725],"domain_scores_gemma":[0.9987795,0.0003312428,0.00009465434,0.0005897417,0.00008367878,0.0001211512],"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":[0.00006564819,0.0002068399,0.2392699,0.0009769173,0.0004746598,0.00001105349,0.001961124,0.5732952,0.0006972238,0.00637418,0.00004593905,0.1766212],"study_design_scores_gemma":[0.0002218681,0.00004952273,0.08701354,0.00004298707,0.0001557635,0.000002237904,0.00003249036,0.9101079,0.00002056369,0.002011744,0.00006998333,0.0002714216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5347889,0.0003075059,0.4624858,0.001552993,0.00008473675,0.0002371924,0.000004122728,0.0003286522,0.0002100631],"genre_scores_gemma":[0.9740131,0.00002134848,0.02553014,0.0000827673,0.00002698674,0.00004962003,0.00008779849,0.00001621517,0.0001720523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4392242,"threshold_uncertainty_score":0.9999644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673072246255539,"score_gpt":0.2678823356117144,"score_spread":0.251151613149159,"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."}}