10 principais gorky.media concorrentes

arzamas.academy

История культуры в лекциях, подкастах и текстах

livelib.ru

Начните челлендж Книжный вызов 2026 на сайте LiveLib: создайте книжный план на год, отмечайте прочитанные книги и наблюдайте за прогрессом чтения в удобном онлайн‑трекере

literratura.org

Электронный литературный журнал. Избранная современная литература в текстах, лицах и событиях

popmatters.com

America’s track and field team won the 2022 olympics?Read More Last Weak Trending Post

daily.afisha.ru

Красный Октябрь – главные новости и все статьи на сайте Афиша Daily. Красный Октябрь – последние релизы организации на сегодня.

lib-gorki.mogilev.by

государственное учреждение культуры "горецкая районная библиотечная сеть"

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.

highonfilms.com

At High on Films, we review films, write insightful essays, and make great recommendation lists based on actors, filmmakers, and scenarios.

kommersant.ru

Сильнейший клуб NFL «Сиэтл Сихокс» продан за $9,61 млрд

catavencii.ro

Cel mai important săptămânal de satiră din România