Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Кредитный скоринг (карты показателей, WoE/IV)× | XGBoost× | |
|---|---|---|
| Область≠ | Финансы | Машинное обучение |
| Семейство≠ | Regression model | Machine learning |
| Год появления≠ | 1997 | 2016 |
| Автор метода≠ | Hand & Henley; Thomas, Edelman & Crook | Chen, T. & Guestrin, C. |
| Тип≠ | Supervised binary classification model | Ensemble (gradient-boosted decision trees) |
| Основополагающий источник≠ | Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Другие названия≠ | Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama | XGBoost, extreme gradient boosting, scalable tree boosting |
| Связанные≠ | 3 | 5 |
| Сводка≠ | Credit scoring is a statistical technique that estimates the probability that a borrower will default on a financial obligation. Using Weight of Evidence (WoE) binning, Information Value (IV) variable selection, and logistic regression, it converts raw applicant data into a single integer score. Formalized by Hand and Henley (1997) and elaborated by Thomas, Edelman, and Crook, the scorecard framework has become the regulatory standard for retail credit risk assessment in banking, lending, and insurance. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateНабор данных ↗ |
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