The All-Russian State University of Justice, Moscow, Russia.
Almazov National Medical Research Centre, Saint-Petersburg, Russia.
Stud Health Technol Inform. 2021 Oct 27;285:259-264. doi: 10.3233/SHTI210610.
Due to the specific circumstances related to the COVID-19 pandemic, many countries have enforced emergency measures such as self-isolation and restriction of movement and assembly, which are also directly affecting the functioning of their respective public health and judicial systems. The goal of this study is to identify the efficiency of the criminal sanctions in Russia that were introduced in the beginning of COVID-19 outbreak using machine learning methods. We have developed a regression model for the fine handed out, using random forest regression and XGBoost regression, and calculated the features importance parameters. We have developed classification models for the remission of the penalty and for setting a sentence using a gradient boosting classifier.
由于与 COVID-19 大流行相关的特殊情况，许多国家实施了自我隔离和限制行动和集会等紧急措施，这也直接影响了各自公共卫生和司法系统的运作。本研究的目的是使用机器学习方法确定俄罗斯在 COVID-19 爆发初期引入的刑事制裁的效率。我们使用随机森林回归和 XGBoost 回归为罚款制定了回归模型，并计算了特征重要性参数。我们使用梯度提升分类器为缓刑和判刑制定了分类模型。
