{"id":"W2426566611","doi":"10.5539/ibr.v9n7p164","title":"Comparing the Performance of Different Data Mining Techniques in Evaluating Loan Applications","year":2016,"lang":"en","type":"article","venue":"International Business Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Loan; Computer science; Decision tree; Random forest; Classifier (UML); Cart; Artificial intelligence; Machine learning; Statistics; Data mining; Actuarial science; Econometrics; Business; Mathematics; Finance; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001158605,0.00007832611,0.0001072418,0.0003419943,0.0001433571,0.0001160777,0.001063894,0.00003300178,0.00007778],"category_scores_gemma":[0.0003879017,0.00004567888,0.00001622303,0.0005990847,0.0001391762,0.0008594511,0.000859131,0.0001141908,0.00002477164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006253607,"about_ca_system_score_gemma":0.00003044459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005299763,"about_ca_topic_score_gemma":0.0002704265,"domain_scores_codex":[0.998571,0.0000209952,0.0002887213,0.0002421782,0.0006877231,0.0001893638],"domain_scores_gemma":[0.9985549,0.0001675414,0.0001209423,0.0003859451,0.0007659608,0.000004687211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008596406,0.0001396344,0.8206971,0.000125372,0.0000130595,9.892342e-7,0.00001986072,0.00003323271,0.009100289,0.005475714,0.0007176835,0.1635911],"study_design_scores_gemma":[0.0002736447,0.000006710358,0.9664754,0.0004553905,0.000004828589,7.622185e-7,0.00005800635,0.02345147,0.0007034144,0.0006361188,0.007854851,0.00007945066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891013,0.00002989721,0.001549969,0.002037054,0.0001581058,0.0003070716,0.00001131298,0.00003394384,0.006771377],"genre_scores_gemma":[0.9987898,0.00005606104,0.0001117457,0.00002869781,0.0006733211,0.0001497373,0.00006858804,0.00001022212,0.0001118363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1635116,"threshold_uncertainty_score":0.1976997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1694375608733418,"score_gpt":0.3956436650767899,"score_spread":0.2262061042034481,"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."}}