Do‘sanov, Raxim (2026) ANSAMBL MASHINAVIY O‘QITISH MODELLARI YORDAMIDA KREDIT RISKINI BAHOLASH. Techscience.uz - Texnika fanlarining dolzarb masalalari, 4 (1). pp. 15-24. ISSN 3030-3702
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Abstract
Mazkur tadqiqotda kredit riskini baholash uchun Bagging, Random Forest, Extra Trees, AdaBoost va Gradient Boosting ansambl algoritmlari qo‘llanildi. Qarz oluvchilarning yoshi, daromadi, kredit reytingi va qarzdorlik darajasini o‘z ichiga olgan ma’lumotlar to‘plami asosida modellar Accuracy, Precision, Recall va F1-score metrikalari yordamida baholandi. Gradient Boosting eng yuqori aniqlikni ko'rsatdi. Natijalar ansambl modellarini bank tizimlarida joriy etish samarali ekanligini tasdiqladi.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Unnamed user with email info@ilmiykutubxona.uz |
| Date Deposited: | 04 Jul 2026 16:21 |
| Last Modified: | 04 Jul 2026 16:21 |
| URI: | https://ilmiykutubxona.com/id/eprint/2178 |
