{"id":"W2094789414","doi":"10.1145/2791060.2791069","title":"Empirical comparison of regression methods for variability-aware performance prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature selection; Computer science; Regression; Regression analysis; Correlation; Artificial intelligence; Feature (linguistics); Machine learning; Product (mathematics); Data mining; Feature engineering; Predictive modelling; Linear regression; Key (lock); Statistics; Mathematics; Deep learning","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":[0.001463901,0.00009479868,0.0001845737,0.0001089643,0.00007920181,0.00004103702,0.0001111667,0.00006625219,0.00005807565],"category_scores_gemma":[0.0004253718,0.00007131013,0.00003334185,0.0003063853,0.00002374385,0.0008059732,0.00007167678,0.00005120174,0.00001539733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003100543,"about_ca_system_score_gemma":0.00003802896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006978307,"about_ca_topic_score_gemma":8.203979e-7,"domain_scores_codex":[0.9991945,0.00002094044,0.0003129319,0.0001943796,0.0001513816,0.0001258781],"domain_scores_gemma":[0.9992048,0.00004933142,0.0001746736,0.0001528951,0.0004071996,0.000011124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000266116,0.0002066692,0.8395469,0.0003856559,0.00001892233,3.742936e-8,0.0002618249,0.0003384691,0.0004173258,0.002043393,0.06775042,0.08876423],"study_design_scores_gemma":[0.001617683,0.00006093261,0.05119279,0.00008274274,0.00008918608,5.028258e-7,0.0005409853,0.7188715,0.008669608,0.006230088,0.2123481,0.0002958392],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2546779,0.00004742499,0.7196012,0.001023531,0.001380619,0.0007291724,0.000001608594,0.0002673352,0.02227122],"genre_scores_gemma":[0.9593312,0.000001630063,0.03932747,0.0001870699,0.0004741196,0.0000331865,0.0001027597,0.00001204309,0.0005304925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7883542,"threshold_uncertainty_score":0.2907943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1059721628742738,"score_gpt":0.3871523876464533,"score_spread":0.2811802247721795,"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."}}