Information System Construction and Research on Preference of Model by Multi-Class Decision Tree Regression

Abstract

Through the construction of customer asset allocation decision preference model based on machine learning, the input variables are set as demographic variables, family economic conditions, personality psychological characteristics and risk attitude, and the output variables are customer asset allocation decision preference choices. machine learning algorithms such as decision tree and support vector machine are used to predict customers’ asset allocation decision preference, and compared with traditional prediction methods. The results show that the machine learning algorithm can predict customers’ asset allocation decision preference to a certain extent, and its performance is more effective than the traditional prediction method. Combined with the sample data, this paper analyzes the impact of venture capital on the growth ability of small and medium-sized enterprises, uses entropy method to evaluate corporate growth ability and corporate governance respectively, and further establishes a regression model to verify the intermediary role of corporate governance. The structured macro-prudential monetary policy rules with different response coefficients to the leverage ratio of different enterprises can effectively improve the level of social welfare, and the combination of optimal response coefficients is different under different kinds of shocks. Venture capital involvement in small and medium-sized enterprises can significantly improve the growth ability of small and medium-sized enterprises, and the support of venture capital can help enterprises improve the level of corporate governance. The experimental results show that the scorecard model constructed by the proposed method has good stability. The more obvious the improvement of the level of social welfare caused by the structural macroprudential monetary policy rules. The accurate prediction of asset allocation decision preference helps to improve customer decision-making efficiency and satisfaction, and reduce the labor costs of financial institutions..