The optimization of random forest algorithms for enterprise financial information management systems is studied in this paper. A random forest algorithm was proposed to improve the data processing capabilities of the financial system. This paper proposes a random forest model on the premise of referring to the latest results of machine learning. The algorithm was introduced into the real estate business financial management system in this paper. First, the samples are divided into training samples and test samples, and the direct prediction method and the two-step prediction method are applied. Mean SR and MAPE were used to compare the prediction accuracy of different algorithms and it was found that the direct prediction method is better. In the algorithm used in this paper, the random forest effect is the best. Then the linear regression, decision tree, neural network and random forest model fitting effects were compared and the best fitting degree of random forest was found.
RANDOM FOREST ALGORITHM OPTIMIZATION OF ENTERPRISE FINANCIAL INFORMATION MANAGEMENT SYSTEM
