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arxiv.uz

«Қоқшол, куйдирги, қутириш» Маърузанинг мақсади: 1. Талабаларга қоқшол, куйдирги, қутириш хакида тушунча бериш 2. Талабаларни қоқшол, куйдирги, қутиришларнинг клиник кечиши билан таништириш 3. Талабаларга қоқшол, куйдирги, қутиришнинг асосий диагностика ва даволаш томойилларини тушунтириш 

scientists.uz

This article examines the creation of programming-based physical models for studying the motion of objects thrown vertically, horizontally, and at an angle to t...

ilmiyanjumanlar.uz

Ushbu maqolada umumtalim maktablarida geometriya darslarini zamonaviy axborot texnologiyalari yordamida oqitish, xususan 3D modellashtirish vositalaridan foydalanish metodikasi yoritiladi. 3D texnologiyalar yordamida fazoviy tasavvur, mavhum tushunchalarni korgazmali qilish, murakkab geometrik figuralarni modellashtirish orqali oquvchilarda tahliliy va ijodiy fikrlashni rivojlantirish imkoniyatlari tahlil qilinadi.

elibrary.ru

рефераты и полные тексты более 39 млн научных публикаций и патентов. электронные версии научно-технических журналов в свободном доступе и по подписке

scholar.google.com

google scholar provides a simple way to broadly search for scholarly literature. search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.

cyberleninka.ru

The banking system is a key sector of the economy and often faces various types of risks. This article focuses on credit risks, which are the main source of financial losses for credit institutions, including banks. Based on real data, a scoring model was developed in this study to assess and optimally manage credit risk, using the XGBoost machine learning algorithm. The model returns score values in the range of 300–850. Quality metrics such as ROC AUC, PR AUC, Precision, Recall, and F1 score were evaluated for the developed model. Within the scope of the research, a new approach was proposed to assess the sensitivity of a machine learning model to changes in variable values. The approach is based on the concept of entropy. The results show that the use of machine learning models makes it possible to assess and optimally manage credit risk, thereby reducing potential losses. Moreover, the proposed new approach, when applied together with the well-known SHAP method, allows for a deeper evaluation of the model and its sensitivity to changes in variable values.

sciencedirect.com

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mdpi.com

MDPI is a publisher of peer-reviewed, open access journals since its establishment in 1996.