This paper describes the process of developing machine learning models for predicting problem states. The formation of decision support systems in problem situations is based on the using of ensemble methods of machine learning: bagging, boosting and stacking. The algorithms of undersampling and oversampling is applied for improving the quality of the models. Using of complex machine learning models reduces the ability to explain the result obtained, therefore various ways of interpreting the constructed models are given. Based on the results of the study, a method for predicting problem states was formed. This approach contributes to the gradual solution of the identified problem situation and the consistent achievement of the goal.

Keywords: machine learning, bagging, boosting, stacking, problem states, data balancing, shap-values