# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Comparing Algorithms
#### 4.1.7 XGBoost Results  
The XGBoost model yielded the worst predictive performance among the tested models, with higher error metrics and a lower correlation between actual and predicted values. Despite its advanced capabilities, the model struggled to capture the underlying patterns in the datasets effectively.  
Figure 12: SVM Time Factor performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Training** & **Testing** & **Validation** \\ \hline Mean Squared Error (MSE) & 2963.599 & 3038.146 & 2939.308 \\ Root Mean Squared Error (RMSE) & 54.439 & 55.119 & 54.215 \\ Mean Absolute Error (MAE) & 40.530 & 38.833 & 37.830 \\ Median Absolute Error (MedAE) & 32.210 & 26.236 & 28.068 \\ Mean Squared Logarithmic Error (MSLE) & 0.008 & 0.008 & 0.008 \\ Root Mean Squared Logarithmic Error (RMSLE) & 0.087 & 0.088 & 0.088 \\ Mean Bias Deviation (MBD) & –\(-0.010\) & \(-0.020\) & \(-0.005\) \\ Mean Absolute Percentage Error (MAPE—Validation) & 6.30\% & 6.11\% & 5.88\% \\ Huber Loss (Validation) & 40.034 & 38.333 & 37.337 \\ Angle between Perfect Fit and Best Fit (degrees) & 13.779 & 12.195 & 13.204 \\ C-index & 0.70 & 0.72 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Support Vector Machine analysis.  
Figure 13 shows the XGBoost performance in predicting 'Time Factor' during training, with each point representing a data observation. Figure 13 shows the XGBoost performance in predicting 'Time Factor' during training, with each point representing a data observation. Figure 13 illustrates the XGBoost's prediction accuracy in the validation phase.  
XGBoost, contrary to expectations, had a higher RMSE, indicating poorer performance than other algorithms in this study, as seen in Table 9. Although it is generally known for its high accuracy and efficiency, it did not perform as well in this application. The higher error rates could be attributed to overfitting or the specific characteristics of the dataset. Furthermore, the complexity and extensive hyperparameter tuning required for XGBoost could have contributed to its suboptimal performance in this context.  
In summary, the experimental results strongly support the selection of ANFIS as the optimal predictive model for our HR data analysis, based on the detailed assessment of key performance metrics as seen in Tables 10 and 11 below.  
Figure 13: XGBoost Time Factor performance.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Training** & **Testing** & **Validation** \\ \hline Mean Squared Error (MSE) & 3623.567 & 7146.835 & 6663.567 \\ Root Mean Squared Error (RMSE) & 60.196 & 84.538 & 81.630 \\ Mean Absolute Error (MAE) & 44.562 & 65.026 & 62.235 \\ Median Absolute Error (MedAE) & 121.825 & 49.720 & 92.346 \\ Mean Squared Logarithmic Error (MSLE) & 0.008 & 0.0179 & 0.016 \\ Root Mean Squared Logarithmic Error (RMSLE) & 0.093 & 0.134 & 0.128 \\ Mean Bias Deviation (MBD) & 0.007 & \(-\)14.770 & \(-\)0.372 \\ Mean Absolute Percentage Error (MAPE—Validation) & 6.90\% & 10.559\% & 9.681\% \\ Huber Loss (Validation) & 44.078 & 64.526 & 61.738 \\ Angle between Perfect Fit and Best Fit (degrees) & 53.754 & 84.379 & 90.875 \\ C-index & 0.85 & 0.82 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 9: XGBoost analysis.