Optimization of Entrepreneurship Education for College Students Based on Improved Random Forest Algorithm

Abstract

As we all know, the random forest algorithm has the advantages of high classification intensity and wide application range. Nevertheless, it still has a lot of room for improvement. This paper introduces the basic idea and working principle of classification algorithm and random forest algorithm, so this paper proposes some improved algorithms on the basis of all current research studies. College students, as the most innovative and adaptable group, have gradually become a development direction of China's education reform to cultivate their innovative employment ability and develop innovative employment education. By constructing a model of the relationship between innovation and entrepreneurship education and college students' employability, this paper examines the impact of innovation and entrepreneurship education mode on college students' employment and entrepreneurship. At the same time, the model is tested by improving the random forest algorithm. The process of talent quality evaluation is to select talents with the best comprehensive quality based on various indicators of students' performance in school, which can be regarded as a classification problem of unbalanced data sets. The improved random forest algorithm proposed in this paper has little difference in precision and recall rate when it is used for talent training evaluation but has a certain degree of improvement in accuracy rate, which meets the design requirements.