Comparison and Application of Logistic Regression and Support Vector Machine in Tax Forecasting

As an indispensable part of economy and society, the proportion of enterprise tax planning in national finance is becoming increasingly important. With the development of economy and information technology, machine learning technology is more and more widely used in various fields, and it is often used to study tax forecasting. However, most of the machine learning methods are difficult to model efficiently because of the problem of tax data with few samples and multi dimensions. To solve this problem, this paper selects the open tax data set provided by an enterprise, and forecasts it based on logistics regression model and support vector machine regression model. We use the average absolute percentage error as the standard to measure the performance difference of the two models in this field. The results of this paper can help enterprise decision-makers to carry out more scientific and reasonable tax planning, and provide some decision-making reference for the national tax department.