چکیده مقاله
The imperative for accurate, robust, and transparent credit risk models is a cornerstone of modern financial stability This study conducts a rigorous comparative evaluation of two leading ensemble learning algorithms, XGBoost and Random Forest, within the context of credit riskassessment Utilizing the benchmark German Credit Data dataset, we implement acomprehensive methodological framework encompassing meticulous preprocessing, class imbalance mitigation via the Synthetic Minority Over sampling Technique SMOTE , and systematic hyperparameter optimization using a ۵ fold cross validate GridSearchCV The predictive efficacy of the models is benchmarked through stratified ۱۰ fold cross validated F۱ scores and Area Under the Receiver Operating Characteristic Curve ROC AUC Critically, this research addresses a significant gap in the literature by moving beyond point estimate comparisons to formally test for statistical significance using the Wilcoxon Signed Rank Test and by dissecting model behavior through SHAP SHapley Additive exPlanations values for enhanced interpretability Our findings reveal a nuanced performance landscape: while Random Forest exhibited a marginally higher mean F۱ score, the Wilcoxon test yielded a p value of ۰ ۰۶۴, indicating no statistically significant difference in the models' predictive capabilities Both models achieved high discriminative power with ROC AUC scores exceeding ۰ ۹۰ SHAP analysis confirmed the primacy of features like 'Duration' and 'Credit Amount' while also uncovering subtle distinctions in feature interaction between the models This study concludes that XGBoost and Random Forest demonstrate functional equivalence in this application, suggesting that model selection for practitioners could be guided by secondary criteria such as computational overhead,scalability, and the specific demands for model transparency
کلیدواژهها
نویسندگان
Navid Goodarzi
University Of Tehran
رزومه علمی کاربران
درباره سیویلیکاتبلیغات در سیویلیکاآمار علمی ایرانتقویم علمی ایرانداده کاوی مقالاتطرح های پژوهشی
شیوه ارجاع
در صورتی که می خواهید در اثر پژوهشی خود به این مقاله ارجاع دهید، به سادگی می توانید از عبارت زیر در بخش منابع و مراجع استفاده نمایید: Goodarzi، Navid،1404،Comparison Between XGBoost and Random Forest in Credit Risk Assessment Using the German Credit Dataset،اولین کنفرانس بین المللی مدیریت، علوم کامپیوتر و هوش مصنوعی،تهران،https://civilica.com/doc/2600113
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی مدیریت، علوم کامپیوتر و هوش مصنوعی30 بهمن 1404 · تهران