# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis
### Analysis 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} & TomeLinks & 0.982 (4.00) & 0.396 (4.009) & 0.920 (4.007) & 0.553 (4.006) \\  & RandomUnderSampler & 0.981 (4.01) & 0.427 (4.104) & 0.811 (4.02) & 0.559 (4.013) \\  & NeighbourhoodCleaningRule & 0.982 (4.000) & 0.378 (4.010) & 0.979 (4.011) & 0.546 (4.013) \\  & AlNNN & 0.984 (4.05) & 0.442 (4.006) & 0.976 (4.005) & 0.669 (4.008) \\  & RepeatedE(EdibleNearestNeighbours & 0.980 (4.01) & 0.331 (4.07) & 0.923 (4.004) & 0.457 (4.015) \\  & EditedNearestNeighbours & 0.984 (4.01) & 0.470 (4.07) & 0.969 (4.004) & 0.633 (4.015) \\  & ClusterCentroids & 0.932 (4.00) & 0.523 (4.007) & 0.209 (4.10) & 0.298 (4.007) \\ \hline \multirow{8}{*}{Oversampling} & SMOTE & 0.970 (0.04) & 0.600 (0.06) & 0.467 (4.03) & 0.525 (4.011) \\  & BorderinsMOTE & 0.971 (4.00) & 0.615 (4.007) & 0.480 (4.014) & 0.559 (4.007) \\  & ADASYN & 0.945 (4.00) & 0.698 (4.07) & 0.239 (4.10) & 0.412 (4.007) \\  & RandomOverSampler & 0.965 (4.00) & 0.574 (4.007) & 0.414 (4.014) & 0.481 (4.007) \\  & SVNASOTE & 0.961 (4.00) & 0.686 (4.07) & 0.388 (4.013) & 0.496 (4.007) \\ \hline \multirow{2}{*}{Combined} & SMOTENN & 0.969 (4.00) & 0.641 (4.07) & 0.463 (4.013) & 0.557 (4.008) \\  & SMOTETOMEK & 0.985 (4.00) & 0.473 (4.008) & 0.992 (4.005) & 0.641 (4.011) \\ \hline \multirow{2}{*}{Proposed} & RSMOTENN & 0.972 (4.004) & 0.619 (4.006) & 0.499 (4.015) & 0.553 (4.011) \\ \cline{2-6}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 12. Performance of Tabnet with Imbalanced Data Processing. Tables 9-12 show that ClusterCentroids have the highest recall on XGBoost, LightGBM, and CatBoost at 0.981, 0.988, and 0.948, respectively. SMOTETOMEK showed the highest precision on LightGBM, Tabnet, and XGBoost at 0.966, 0.992, and 0.956, respectively. SMOTETOMEK also showed the highest accuracy on LightGBM, Tabnet, and XGBoost at 0.99, 0.985, and 0.99, respectively. The ClusterCentroids method iteratively replaces the non-dropout data with dropout data based on the median value of the dropout data. However, this method was not suitable because the dropout precision and dropout F1 were significantly lower in all models and the matrix varied from measurement to measurement. However, the RandomOverSamplerSMOTEENN method had high dropout recall while maintaining dropout precision. As seen from Tables 9-12, since the data of Gyeongsang National University are imbalanced, we could not find a model with both high dropout precision and high dropout recall. Therefore, we selected a model with high dropout precision and a model with high dropout recall, then created a hybrid model to increase both metrics. We selected the group with the highest dropout precision and dropout recall and sorted by the highest ROC curve and dropout F1 to select the top model. XGBoost with SMOTE is the model with the highest true positive rate when the false positive rate is low on the ROC curve. Moreover, the dropout precision of XGBoost with SMOTE is 0.947 and its dropout F1 is 0.803, which makes it the highest-ranked model. CatBoost's RandomOverSamplerSMOTEENN is the model with the highest area under the curve and highest dropout recall of 0.752 on average across grades in the ROC curve comparing imbalanced data processing in Figure 5a. The model was also stable over repeated measurements for all other metrics including the dropout recall. Figure 5a,b show the ROC curve of XGBoost, a candidate for the high precision group, and CatBoost, a candidate for the high dropout recall group, using freshman data, which exhibits the highest dropout number. The yellow line represents \"Non-Processing\", the blue line represents \"TomekLinks\", the green line represents \"SMOTE\", and the brown line represents \"SMOTEENN\". The red represents \"RandomOverSamplerSMOTEENN\", denoted as \"SMOTEENNRD\" in the graphs.