## 3 Results
### WBM Model
_MacroAccuracy_, calculated as the mean accuracy across all classes, gave equal weight to each well-being category, providing insights into the model's effectiveness even for less frequent states. In contrast,MicroAccuracy_ pooled predictions across all classes, reflecting the model's overall accuracy, which is especially useful in imbalanced datasets. This combination of high _MacroAccuracy_ and _MicroAccuracy_ confirms the model's adaptability in diverse classification scenarios, ensuring accurate general predictions and sensitivity to less common well-being states. This rigor in model evaluation reinforces the WBM's potential to monitor and predict nuanced employee well-being trends, an advantage over more conventional approaches. To contextualize these metrics, we compared them against industry standards in well-being prediction models, which typically range from 85 to 90% in terms of macro accuracy and 90 to 93% in terms of micro accuracy. The WBM's accuracy metrics exceed these benchmarks, highlighting its effectiveness in real-world applications. For instance, several studies in related fields, such as ML models for predicting cardiovascular diseases, report accuracy values in a similar range. For example, in heart disease prediction models, top-performing algorithms such as Random Forest and AdaBoost achieved accuracy levels around 90% and higher, reaching up to 93-95% with advanced techniques like ensemble methods [67; 68]. In \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range** & **Description** \\ \hline L1 Regularization & \{0.0, 0.01, 0.1, 1.0\} & Higher values reduce the number of non-zero parameters, which helps with feature selection. The selected values allow exploration from no regularization (0.0) to highest (1.0) level. \\ \hline L2 Regularization & \{0.0, 0.01, 0.1, 1.0\} & The chosen values range from no regularization (0.0) to maximum value (1.0) levels, helping to balance model generalization. \\ \hline Optimization Tolerance (Learning Rate) & \{1 \(\times 10^{-4},1\times 10^{-5}\), \(1\times 10^{-6},1\times 10^{-7}\)\} & Smaller values (\(1\times 10^{-7}\)) mean the algorithm will seek higher precision before stopping, while larger values (\(1\times 10^{-4}\)) allow it to terminate earlier. The range was selected to test the trade-off between speed and accuracy in convergence. \\ \hline History Size (Epochs) & [20, 500, 1000] & The history size controls the number of past iterations the algorithm stores for optimization. Smaller sizes (20) limit memory usage but might slow convergence, while larger sizes (500, 1000) allow for faster, more accurate updates at the cost of increased memory usage. The range covers minimal to extensive memory use scenarios, balancing speed and resource management. \\ \hline Enforce Non-Negativity (Dropout) & [True, False] & This parameter controls whether the model should restrict coefficients to non-negative values. Enforcing non-negativity (True) is useful when it is known that negative weights are not meaningful in the problem context, whereas allowing negative weights (False) provides more flexibility to the model. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Range and description of the parameters. the mental well-being domain, ML models like Random Forest and adaptive boosting are also frequently reported to achieve similar high accuracy in identifying negative mental health conditions, such as anxiety or depression [67]. This level of accuracy underscores the WBM's potential for deployment in organizational well-being initiatives. The _LogLoss_ of 0.4814 confirms that the WBM makes correct predictions with well-calibrated probability estimates. Although there is room for fine-tuning these predictions' confidence, a _LogLoss_ of this level is quite favorable. It suggests that the model avoids making overly confident yet incorrect predictions, balancing accuracy with the precision of its probability outputs.