Study on Influencing Factors and Prediction Based on Random Forest of Doctoral Dissertations

Abstract

Doctoral dissertation is one of the most important parts in the doctoral degree program. This paper analyzes the data of doctoral dissertations in a university for three consecutive years, and investigates the factors related to the quality of doctoral dissertations from the aspects of students' characteristics and cultivation methods. The results show that the enrollment age, study period, mode of study and subject categories, whether cross-specialty, etc., have significant impact on the quality of doctoral dissertations. Then, a prediction model based on the weighted Random Forest (RF) is presented to predict the quality of doctoral dissertation, which is effective for unbalanced data and improves the generalization ability of the original RF, the encouraging result of 81.29% prediction rate has been obtained, which provides objective evidence for doctoral degree program management.