Reliability Test of Control Loop Information in New Power Load Management System

This paper discusses the application of stochastic forest in the detection of new power load management system, and deals with regression and classification problems by integrating multiple decision tree subtrees. Since each decision tree produces one result, when the number of input samples is N, N classification results will be produced. Finally, all classification results will be selected through a voting mechanism. Random forest algorithm combines Bagging idea and decision tree, where a single decision tree as a weak classifier has the defect of weak generalization ability, but the integrated model becomes a strong classifier. This ensemble learning approach improves the accuracy of classification and prediction, but at the expense of interoperability, i.e. the difficulty of determining decisive variables. Improve the performance of the overall model by combining multiple base models. CART decision trees determine which attribute should be given a higher weight based on the minimum Gini index, which provides a measure of descriptive impurity content and is calculated in a way designed to optimize the tree's performance.