Application of the «random forest» model in the analysis of production business processes

The relevance of the article is due to the increase in the volume of data generated by industrial enterprises in real time and the need to identify patterns affecting the efficiency of production processes. Traditional data analysis methods are often insufficient to account for the complex nature of this data, which creates the need for modern technologies such as machine learning. The purpose of the study is to study the advantages and disadvantages of forming the “Random Forest” model when using it in enterprises, the study of its role in improving the efficiency and optimization of production systems. The main objectives of this study are to determine the method of construction and methods of operation of the “Random Forest” model, to identify ways of using the “Random Forest” model in the context of analysis of production business processes, to identify advantages, disadvantages and to make recommendations for the use of the “Random Forest” model in industry. Methodology: The research is based on the analysis of theoretical and practical approaches to the use of the “Random Forest” model. Results: The research conducted has shown the high efficiency of the “Random Forest” model in forecasting and analyzing production data. Key advantages have been identified, such as resistance to overfitting, the ability to work with large amounts of data, and high prediction accuracy. Disadvantages related to the duration of training and sensitivity to missing data are also noted. Conclusions: The use of the “Random Forest” model can have a positive impact on the optimization of production processes and management decision-making. It is recommended to use high-quality datasets and parallel application of other machine learning models to improve the accuracy of forecasts.