{"id":"W4380894276","doi":"10.1016/j.eswa.2023.120682","title":"TOPSIS-based comprehensive measure of variable importance in predictive modelling","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Interpretability; Computer science; Feature selection; Robustness (evolution); Data mining; Variable (mathematics); Machine learning; Measure (data warehouse); TOPSIS; Curse of dimensionality; Artificial intelligence; Operations research; Mathematics","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":[0.0002044176,0.0001262523,0.00022494,0.0001429788,0.00008484148,0.00003965465,0.000478017,0.00006739794,0.000001552371],"category_scores_gemma":[0.000004968019,0.0001104051,0.00002505123,0.001300244,0.00004163661,0.0001538148,0.00004066414,0.0001091056,0.0000153237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004811187,"about_ca_system_score_gemma":0.0001884531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003142895,"about_ca_topic_score_gemma":0.000007484289,"domain_scores_codex":[0.9987084,0.00005190678,0.0003366953,0.0003915885,0.0002874541,0.0002239147],"domain_scores_gemma":[0.9987473,0.0001100783,0.0001426188,0.0006513293,0.0002775428,0.00007116224],"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":[0.0000110119,0.00006575867,0.0006653837,0.00005534497,0.0000173395,0.000002559301,0.0007263577,0.9117563,0.0008857996,0.08496799,0.000493265,0.0003528543],"study_design_scores_gemma":[0.0002274465,0.00003680512,0.00008684561,0.0001753604,0.000002499913,0.000002910596,0.0002102381,0.9961902,0.000248293,0.0008991845,0.001789515,0.0001306414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001323747,0.0005667157,0.9960649,0.0001613753,0.0000608226,0.0005808672,0.0000129548,0.0002399834,0.000988635],"genre_scores_gemma":[0.9532306,0.00002098567,0.044941,0.00007386167,0.0000510498,0.001574769,0.00001626142,0.00001355015,0.00007789428],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9519069,"threshold_uncertainty_score":0.450219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04060741544420712,"score_gpt":0.2660326687072231,"score_spread":0.225425253263016,"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."}}