## 4 Experiments
### Results
#### 4.6.1 Results on IBM Dataset  
In order to evaluate our proposed model, we also tested several widely used classical machine learning and deep learning models for comparison. The overall performance of these models on the IBM dataset is summarized in Table 5. The table includes a variety of evaluation measures, including accuracy, precision, recall, F1-score, AUC, and training time, assessed using 5-fold cross-validation. These findings provide insightful information on the positive aspects of the proposed model over alternative strategies, assisting in the identification of the best methods for predicting employee attrition. According to the results, our proposed model outperforms other baseline deep learning and machine learning models, achieving an accuracy of 89.65% and an F1-score of 61.61%. A closer analysis reveals that the second-best accuracy, following our proposed model, was achieved by Transformer, with an accuracy of 88.77%. In terms of the F1-score, the next best performances were obtained by Bi-GRU and Bi-LSTM, with F1-scores of 56.15% and 53.56%, respectively. In terms of training time, the proposed model takes longer than other models due to its bi-directional and ensemble structure. However, since the data for this specific task are typically small, this drawback can be ignored. The loss and validation loss diagrams of the proposed model on the IBM dataset are presented in Figure 7a, while Figure 7b illustrates the accuracy and validation accuracy diagrams for the model based on the IBM dataset. The loss diagram illustrates the training loss values, showing how the model's predictions improve and align with the actual labels throughout the training process. The validation loss diagram, on the other hand, depicts the fluctuation in loss values on the validation set during evaluation. \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) & 77.82 & 34.39 & 40.59 & 36.97 & 62.81 & 00:01 \\ Random Forest (RF) & 85.98 & 80.37 & 17.88 & 28.95 & 58.49 & 00:10 \\ KNN & 82.85 & 40.50 & 16.54 & 22.95 & 56.13 & 00:01 \\ Logistic Regression & 87.68 & 74.66 & 36.30 & 48.58 & 66.97 & 00:01 \\ Ada Boost & 87.61 & 72.09 & 41.27 & 51.73 & 68.92 & 00:11 \\ Gradient Boosting & 87.41 & 66.99 & 45.34 & 53.69 & 70.47 & 00:06 \\ XG Boost & 86.93 & 70.55 & 33.73 & 45.10 & 65.47 & 00:03 \\ Cat Boost & 87.21 & 69.50 & 38.24 & 49.07 & 67.45 & 00:12 \\ Deep Learning Models & & & & & & \\ MLP & 86.93 & 69.69 & 33.04 & 44.53 & 65.17 & 00:15 \\ CNN & 87.21 & 76.81 & 29.44 & 42.22 & 63.90 & 00:20 \\ LSTM & 86.12 & 58.05 & 43.15 & 48.10 & 68.78 & 00:24 \\ Bi-LSTM & 86.59 & 58.76 & 49.44 & 53.56 & 71.56 & 00:33 \\ GRU & 86.53 & 61.28 & 44.42 & 51.27 & 69.48 & 00:21 \\ BI-GRU & 86.87 & 60.67 & 52.39 & 56.15 & 72.89 & 00:29 \\ Transformer & 88.77 & 57.78 & 47.97 & 51.86 & 72.23 & 02:21 \\ Proposed Model & **89.65** & **69.81** & **56.30** & **61.61** & **77.58** & **00:59** \\ (Ensemble Bi-TCN) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the proposed model compared to various models on the IBM dataset. The accuracy diagram visually represents the model's performance during training, highlighting its ability to predict employee attrition. Meanwhile, the validation accuracy diagram reflects the model's generalization ability by tracking accuracy trends on the validation set across multiple epochs. These diagrams provide a thorough understanding of the model's overall performance and optimization. According to the diagrams, the training loss starts high and drops rapidly within the first few epochs. It then declines gradually with a low slope, reaching its lowest point around the 50 th epoch. The validation loss follows a similar trend but exhibits slightly more fluctuations. After the 30 th epoch, it becomes more stable. Both losses eventually stabilize at a low value, indicating that the model is learning effectively.