Analysis of interpreted machine learning methods for predicting the execution of government contracts in the field of electric power

Abstract

The power industry plays a key role in ensuring the energy security of the state. Sustainable and reliable functioning of the energy system requires the fulfillment of contracts with strictly observed deadlines and quality of work. This article describes an algorithm for selecting methods for interpreting machine learning models, analyzes gradient boosting-based machine learning methods recommended for solving prediction tasks in the field of power engineering, and presents methods for interpreting the results. The authors have achieved good results in training models and determined objective assessments of the contribution of each feature to solving the prediction tasks of contract fulfillment. This research is significant in the context of ensuring the efficiency and transparency of public procurement and can be beneficial for specialists and government bodies responsible for monitoring contract fulfillment in the field of power engineering.