{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01840445,0.002136412,0.001260735,0.003983123,0.0004073185,0.001121801,0.001922697,0.00145103,0.0007818927],"category_scores_gemma":[0.05533333,0.0004440395,0.001016878,0.003150124,0.0005326477,0.002273577,0.0008021715,0.002117564,0.0006810507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008574132,"about_ca_system_score_gemma":0.0006722215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005948218,"about_ca_topic_score_gemma":0.003854723,"domain_scores_codex":[0.9924049,0.004337255,0.0004338272,0.0012825,0.001261543,0.0002799295],"domain_scores_gemma":[0.8835044,0.1015528,0.004547489,0.005103504,0.004735782,0.0005560621],"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":[0.001091853,0.0006229086,0.09031762,0.0003978392,0.0008890188,0.0001101845,0.0002674262,0.660602,0.002491836,0.001804851,0.003805905,0.2375985],"study_design_scores_gemma":[0.00002296726,0.0001702671,0.01218981,0.00003437728,0.00005144464,0.00006056876,0.00005455372,0.9848042,0.00106673,0.001064808,0.0004488188,0.00003157764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6676199,0.00573213,0.3158729,0.0008486086,0.0001809725,0.0001603117,0.001844467,0.004646245,0.003094304],"genre_scores_gemma":[0.9256096,0.0005987362,0.07058537,0.00009928862,0.00007928618,0.000113885,0.002044658,0.0002403049,0.0006288509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01840445,"threshold_uncertainty_score":0.09733313,"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."}}