{"id":"W3171070551","doi":"","title":"A Precise Performance Analysis of Support Vector Regression","year":2021,"lang":"en","type":"article","venue":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Support vector machine; Regression analysis; Mathematics; Regression; Linear regression; Star (game theory); Statistics; Computer science; Algorithm; Artificial intelligence; Mathematical analysis","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","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001341727,0.00022491,0.000624821,0.004803062,0.002195295,0.00005292147,0.002676221,0.0003243452,0.000007948049],"category_scores_gemma":[0.0003014902,0.0002524443,0.00008583102,0.01712067,0.0180823,0.001061038,0.002227498,0.0003715044,0.00000137186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498962,"about_ca_system_score_gemma":0.001204356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002735921,"about_ca_topic_score_gemma":0.0000545224,"domain_scores_codex":[0.9970151,0.00004004435,0.0002848308,0.001153085,0.0009512794,0.0005557043],"domain_scores_gemma":[0.9960464,0.00006700063,0.0006089854,0.001051689,0.002069692,0.0001562266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006049783,0.0003156454,0.277087,0.0002041636,0.0002888912,0.0003712513,0.004895846,0.0001523668,0.5708865,0.02377658,0.00009797113,0.1218633],"study_design_scores_gemma":[0.002989264,0.002020769,0.1636481,0.001504391,0.001336026,0.0009157559,0.04349427,0.1119576,0.6606797,0.001570837,0.008135293,0.001748048],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993984,0.0002254681,0.001633401,0.001635938,0.0001463171,0.0001037988,0.000003533645,0.0002489875,0.002018556],"genre_scores_gemma":[0.9874781,0.000326629,0.01178407,0.00001054798,0.000004423827,8.946181e-8,8.572695e-7,0.000004439529,0.0003908177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201152,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004851419409060227,"score_gpt":0.1900184164693654,"score_spread":0.1851669970603052,"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."}}