## 4 Experiments
### Results
#### 4.6.2 Results on Kaggle Dataset  
We also evaluate our proposed model on another employee attrition dataset, comparing its performance with baseline classical machine learning and deep learning models. The overall performance of these models on the Kaggle dataset is summarized in Table 7. This table presents various evaluation metrics, including accuracy, precision, recall, F1-score, AUC, and training time, all assessed using 5-fold cross-validation. The results indicate that the proposed model, RF model, and XGBoost model achieve the best performance on this dataset. The RF model attains the highest accuracy at 97.85%, followed closely by the proposed model with 97.83%, showing only a marginal difference. However, when considering the F1-score metric, the proposed model outperforms the RF model, achieving an F1-score of 95.56% compared to 95.49%. XGBoost ranks next, with an accuracy of 97.62% and an F1-score of 95.10%. Regarding the AUC metric, the proposed model achieves the highest value at 96.94%, followed by the XGBoost and RF models with AUC scores of 96.19% and 96.16%, respectively. Figure 12a presents the accuracy and validation accuracy diagrams for the proposed model using the Kaggle dataset, while Figure 12b illustrates the loss and validation loss diagrams for the proposed model on the same dataset. Figure 11: The most influential features in the IBM dataset affecting employee turnover prediction. The loss diagrams illustrate that both training and validation loss decrease rapidly, reaching very low values and stabilizing after approximately 20 epochs. This trend suggests that the model is learning efficiently and has converged well. Additionally, the validation loss closely follows the training loss, indicating minimal overfitting. According to the accuracy diagrams, the training and validation accuracy curves start low and rise rapidly. After 20 epochs, both curves stabilize around 97%, with slight fluctuations in validation accuracy. The close alignment between validation and training accuracy suggests that the model generalizes well. Figure 13 also presents the ROC-AUC (Receiver Operating Characteristic-Area Under Curve) diagram for the proposed model on the Kaggle dataset. The steep rise in the ROC curve indicates that the model achieves a high True Positive Rate while maintaining a low False Positive Rate. The dashed line represents random guessing (AUC = 0.5), and since the model's curve is well above this line, it demonstrates \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy \%** & **Precision \%** & **Recall \%** & **F1-Score \%** & **AUC \%** & **Time (M:S)** \\ \hline Machine Learning Models & & & & & & \\ Decision Tree (DT) & 95.98 & 90.82 & 93.11 & 91.94 & 95.01 & 00:01 \\ Random Forest (RF) & **97.85** & **98.29** & 92.85 & 95.49 & 96.16 & 00:21 \\ KNN & 92.97 & 82.46 & 90.77 & 86.41 & 92.23 & 00:02 \\ Logistic Regression & 77.64 & 58.46 & 31.23 & 40.66 & 62.01 & 00:02 \\ Ada Boost & 93.47 & 86.97 & 86.35 & 86.66 & 91.06 & 00:21 \\ Gradient Boosting & 93.93 & 88.03 & 87.28 & 87.65 & 91.70 & 00:17 \\ XG Boost & 97.62 & 96.90 & 93.38 & 95.10 & 96.19 & 00:04 \\ Cat Boost & 94.32 & 89.33 & 87.48 & 88.36 & 92.02 & 00:26 \\ Deep Learning Models & & & & & & \\ MLP & 77.71 & 58.99 & 31.24 & 40.09 & 62.04 & 01:34 \\ CNN & 77.87 & 57.83 & 39.29 & 45.95 & 64.84 & 01:31 \\ LSTM & 96.17 & 94.47 & 89.81 & 92.04 & 94.05 & 02:02 \\ Bi-LSTM & 96.35 & 93.14 & 91.99 & 92.54 & 94.89 & 02:29 \\ GRU & 96.05 & 93.80 & 89.94 & 91.82 & 93.99 & 01:47 \\ BI-GRU & 96.55 & 93.86 & 92.02 & 92.93 & 95.03 & 02:12 \\ Transformer & 96.92 & 93.31 & 94.28 & 93.79 & 96.03 & 16:07 \\ Proposed Model & & 97.83 & 95.95 & **96.37** & **95.56** & **96.94** & 06:32 \\ (Ensemble Bi-TCN) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the proposed model compared to various models on the Kaggle dataset. Figure 12: The loss and accuracy diagrams of the proposed model on Kaggle dataset. that the proposed model significantly outperforms random guessing.