# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis
### Analysis of Imbalanced Data Processing Methods  
As we discussed in Section 3, the dropout record in our data is imbalanced and is a minor class, which has an adverse effect on producing high precision and recall rates in Figure 4a-d.  
To produce a high recall rate, which other methods failed to do, we used RandomOverSamplerSMOTEEENN. In this section, we measure the performance (accuracy, recall, precision, F1) of XGBoost, LightGBM, CatBoost, and Tabnet combined with different over/under sampling algorithms. The undersampling methods we tested are TomekLinks [12], RandomUnderSampler [13], NeighborhoodCleaningRule [22], AllKNN [23], RepeatedEdit-edNearestNeighbours [23], EditedNearestNeighbours [14], and ClusterCentroids [24]. The oversampling methods we tested are SMOTE [15], BorderlineSMOTE [16], ADASYN [17], RandomOverSampler [25], and SVMSMOTE [26]. We tested the combined methods SMOTEENN [18] and SMOTETOMEK [19]. The proposed method, RandomOverSamplerSMOTEEENN, combines the advantages of the RandomOverSampler and SMOTEENN methods. There were ensemble models, but we excluded them because of their complexity. Tables 9-12 show the experiment results of XGBoost, LightGBM, CatBoost, and Tabnet combined with different imbalanced data processing methods, respectively. Figure 4: Comparison of Imbalanced Data Processing Methods. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **Processing** & **Accuracy** & **Dropout Recall** & **Dropout Precision** & **Dropout F1** \\ \hline \multirow{8}{*}{Undersampling} & TomekLinks & 0.990 (\(\pm\)0.00) & 0.695 (\(\pm\)0.09) & 0.965 (\(\pm\)0.01) & 0.808 (\(\pm\)0.06) \\  & RandomUndersampler & 0.902 (\(\pm\)0.04) & 0.845 (\(\pm\)0.07) & 0.199 (\(\pm\)0.08) & 0.323 (\(\pm\)0.09) \\  & NeighbourhoodCleaningRule & 0.990 (\(\pm\)0.01) & 0.697 (\(\pm\)0.07) & 0.939 (\(\pm\)0.10) & 0.802 (\(\pm\)0.09) \\  & \multirow{2}{*}{RepeatedEditionK NearestNeighbours} & 0.990 (\(\pm\)0.01) & 0.697 (\(\pm\)0.07) & 0.936 (\(\pm\)0.09) & 0.799 (\(\pm\)0.09) \\  & EditedNearestNeighbours & 0.990 (\(\pm\)0.01) & 0.701 (\(\pm\)0.07) & 0.951 (\(\pm\)0.09) & 0.807 (\(\pm\)0.09) \\  & ClusterControids & 0.122 (\(\pm\)0.02) & 0.958 (\(\pm\)0.07) & 0.030 (\(\pm\)0.07) & 0.058 (\(\pm\)0.08) \\ \hline \multirow{8}{*}{Oversampling} & SMOTE & 0.990 (\(\pm\)0.02) & 0.694 (\(\pm\)0.07) & 0.959 (\(\pm\)0.07) & 0.805 (\(\pm\)0.08) \\  & BorderlineSMOTE & 0.990 (\(\pm\)0.02) & 0.695 (\(\pm\)0.07) & 0.959 (\(\pm\)0.07) & 0.805 (\(\pm\)0.08) \\  & ADASYN & 0.990 (\(\pm\)0.02) & 0.694 (\(\pm\)0.07) & 0.952 (\(\pm\)0.07) & 0.803 (\(\pm\)0.08) \\  & RandomOverSampler & 0.984 (\(\pm\)0.02) & 0.735 (\(\pm\)0.07) & 0.703 (\(\pm\)0.07) & 0.719 (\(\pm\)0.08) \\  & SVNSMOTE & 0.990 (\(\pm\)0.02) & 0.696 (\(\pm\)0.07) & 0.959 (\(\pm\)0.07) & 0.806 (\(\pm\)0.08) \\ \hline \multirow{2}{*}{Combined} & SMOTENN & 0.990 (\(\pm\)0.02) & 0.696 (\(\pm\)0.07) & 0.949 (\(\pm\)0.06) & 0.800 (\(\pm\)0.06) \\  & SMOTECNN & 0.990 (\(\pm\)0.02) & 0.719 (\(\pm\)0.07) & 0.830 (\(\pm\)0.06) & 0.770 (\(\pm\)0.06) \\ \hline \end{tabular}
\end{table} 
Table 10: Performance of LightGBM with Imbalanced Data Processing.