## 4 Experiments
### Results
#### 4.6.1 Results on IBM Dataset
Additionally, the validation loss does not significantly diverge from the training loss, suggesting the absence of severe overfitting. The stabilization of loss after a few epochs implies that the model reaches an optimal point without unnecessary fluctuations. Furthermore, both training and validation accuracy generally follow an increasing trend, with some fluctuations. These fluctuations may indicate an imbalance in the data, but the overall upward trend highlights the model's efficiency. Figure 8 also presents the ROC-AUC (Receiver Operating Characteristic-Area Under Curve) diagram for the proposed model on the IBM dataset. This graph offers insightful information about the model's classification performance, which aids in assessing its efficacy. We can ensure a more precise and successful binary classification method by making well-informed selections regarding threshold modifications and model selection by examining the ROC-AUC curves. According to the ROC-AUC diagram, the curve rises steeply, reaching a high True Positive Rate (TPR) early, indicating strong performance. A low False Positive Rate (FPR) at the beginning suggests that the model correctly classifies many positive cases before making errors. Additionally, the Area Under the Curve (AUC) is 0.78, demonstrating the model's good discriminative ability. In additional experiments, to emphasize the critical importance of data augmentation and class balancing, we utilize a GAN-based data augmentation and three oversampling techniques: Random Oversampling, SMOTE, and ADASYN. These techniques are applied to improve the performance of the proposed model. Table 6 compares the results of using these different techniques in conjunction with the proposed model. Figure 7: The loss and accuracy diagrams of the proposed model on IBM dataset. For GAN-based data augmentation, we first generated 3000 synthetic data instances based on the original IBM dataset with 1500 belonging to the 'Yes' attrition group and 1500 to the 'No' attrition group. To ensure the reliability of our results and prevent any bias from synthetic data correlations, we use only the original data for testing. Given that we employ 5-fold cross-validation, in each iteration, 20% of the original data is set aside as test data, while the remaining 80% is combined with the generated synthetic data to form the training set. Consequently, in each iteration, the training set consists of 4176 instances, which is nearly three times larger than the original dataset and effectively addresses the class imbalance by achieving a nearly balanced distribution, while the test set comprises 294 instances. For all three other oversampling methods, the oversampling is applied exclusively to the training data in each iteration. The results indicate that nearly all techniques produced similar and closely matched outcomes. Notably, all these methods enhanced the efficiency of the proposed model by approximately 2.5%. This improvement can be attributed to the increased volume and improved balance of the training data. However, the model achieved the highest accuracy when using GAN data augmentation, while the best F1-score was obtained with ADASYN. When comparing these techniques, we can state that GANs produce data that closely mimic genuine samples by learning the entire data distribution and creating completely new synthetic samples for every class. On the other hand, oversampling techniques expand the minority class's sample size without revealing the distribution as a whole. The Random Oversampling method randomly chooses and duplicates existing instances from the minority class until the class distribution becomes more balanced. SMOTE creates synthetic samples by extrapolating from minority class samples that already exist. Between a randomly chosen data point and one of its K-Nearest Neighbors, a new synthetic sample