{"id":"W4225087921","doi":"10.1109/mlke55170.2022.00047","title":"Credit Card Approval Predictions Using Logistic Regression, Linear SVM and Naïve Bayes Classifier","year":2022,"lang":"en","type":"article","venue":"","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Logistic regression; Credit card; Support vector machine; Computer science; Naive Bayes classifier; Machine learning; Artificial intelligence; Classifier (UML); Bayes' theorem; Credit risk; Linear regression; Predictive modelling; Data mining; Bayesian probability; Finance","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":[0.0002069039,0.0001670785,0.0001778718,0.0001807765,0.001030072,0.000148065,0.000137208,0.00006239423,0.0006482845],"category_scores_gemma":[0.00009556971,0.0001439242,0.00006653143,0.0003863385,0.00009202626,0.0005340546,0.0003948234,0.0002379867,0.00001713828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005469044,"about_ca_system_score_gemma":0.00003323479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001184644,"about_ca_topic_score_gemma":0.00005765457,"domain_scores_codex":[0.9987956,0.00001584965,0.000251849,0.0003503286,0.0003360146,0.0002503676],"domain_scores_gemma":[0.9995268,0.00002320523,0.0001433212,0.0001899973,0.0000995992,0.00001707483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000857748,0.001403343,0.2209535,0.001277361,0.0003479162,0.0002586666,0.0004916053,0.03519143,0.004707547,0.2575442,0.4328173,0.04414938],"study_design_scores_gemma":[0.0009433967,0.00006010479,0.04807047,0.00006705165,0.0002428802,0.00002822391,0.001503705,0.6400814,0.00002378166,0.003557015,0.3049244,0.0004975643],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8743337,0.0006675014,0.04397244,0.001867358,0.006569893,0.001013613,0.0002518359,0.001159238,0.07016446],"genre_scores_gemma":[0.9954836,0.00001209172,0.0004306849,0.000448577,0.002129766,0.000051336,0.0001383621,0.00002612098,0.001279407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6048899,"threshold_uncertainty_score":0.7922586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05126814371435433,"score_gpt":0.2552854398403598,"score_spread":0.2040172961260055,"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."}}