## 5 Evaluation
### Classification and Prediction
#### 5.2.2 Prediction Results  
Combining the above feature selection results with the classification results, we selected the SVM algorithm with the highest accuracy to predict whether first-year graduates would be able to publish papers by integrating the 11 features selected from the results of Lasso, Elastic Net and OMP. In the meantime, considering the aim of this study is to explore the factors influencing the academic situation of graduates and to use publication as a measure, it is more practical to focus on the group of graduates who are more likely to publish papers, i.e., first-year graduates whose plan after graduation is to obtain a PhD. Thus, we conduct a comparative analysis of the specific factors that predict whether or not a paper will be published for these graduates. If not, the reasons why they are unable to publish are analyzed and suggestions are proposed for them. The results are shown in Figure 2d. To further discuss the predictive effect, we select one typical factor in each of A, C and D grades for detailed analysis.  
**Supervisor's requirements for paper publication (Tguidance4).** As shown in Figure 7a, all of the graduate students who can publish papers are more or less required for paper publication from their supervisors, and 91% of whom have supervisor's guidance as well, whereas 25% of the graduates who cannot publish papers have no requirements from their supervisors. Thus, it can be concluded that the lack of requirements of supervisors is the key reason why these graduates cannot publish papers.  
**Participation of supervisor's projects (Tguidance3).** As shown in Figure 7b, the majority of graduate students who are able to publish papers involved in their supervisor's projects, with only 18% not involved. In contrast, 75% of those who are unable to publish do not participate in their supervisor's projects, and the remaining 25% take part in only one of their supervisor's projects. This shows that deep participation in supervisor's research projects can effectively improve the academic ability of graduates.  
**Frequency of academic discussion (Tguidance5).** As shown in Figure 7c, 77% of graduate students who are capable of publishing papers have discussions with their supervisors as frequently as once a week. Nevertheless, as many as 50% of those who are unable to publish have discussions with their supervisors less frequently than once a week, and 20% of them do not talk to their supervisors at all. This suggests that a high frequency of communication and discussion can be of great help in promoting the research progress of graduates.  
Figure 6: AUC values for five classification algorithms. (**a**) shows the composite comparison and (**b–d**) show the specific AUC values for the different methods, i.e., original, Lasso, OMP.  
Figure 7: Predicted results of whether first-year graduate students will be able to publish papers. (**a–c**) show the results for typical factors in grades A, C and D, respectively, i.e., Supervisor’s requirementsfor paper publication (Tguidance4), Participation of supervisor's projects (Tguidance3) and Frequency of academic discussion (Tguidance5).  
In summary, there are some specific suggestions for the first-year graduates. Firstly, choose a face-to-face interaction with your supervisor and be brave enough to ask questions as frequently as possible. Next, actively participate in your supervisor's projects, not too much, but in a way that allows you to fully exercise yourself and gain academic knowledge. Finally, take part in academic training and lectures; although there is not a strong positive correlation between this and publication, the more times the better in terms of the prediction results.