Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Z-критерий на Алтман: Прогнозиране на корпоративен фалит× | XGBoost× | |
|---|---|---|
| Област≠ | Финанси | Машинно обучение |
| Семейство≠ | Regression model | Machine learning |
| Година на възникване≠ | 1968 | 2016 |
| Създател≠ | Edward Altman | Chen, T. & Guestrin, C. |
| Тип≠ | Multiple discriminant analysis scoring model | Ensemble (gradient-boosted decision trees) |
| Основополагащ източник≠ | Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Други названия≠ | Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru | XGBoost, extreme gradient boosting, scalable tree boosting |
| Свързани≠ | 3 | 5 |
| Резюме≠ | The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone. | 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 от данни ↗ |
|
|