cross-validation

The article addresses the problem of optimizing linear regression models under conditions of high dimensionality and multicollinearity, which are typical for modern machine learning applications. The relevance of the study is обусловлена the need to ensure a balance between model generalization ability and interpretability, especially when dealing with noisy and limited datasets.

Crime rate forecasting is a critical element in the development of strategies for sustainable socio-economic growth in a rule-of-law state. Accurate forecasting becomes particularly important in times of economic instability and geopolitical crises, as is the case in Ukraine. This article explores the problem of constructing and applying autoregressive integrated moving average (ARIMA) models to predict the total number of crimes committed in Ukraine.