## 3 Results
### WBM Model  
The LbfgsMaximumEntropy algorithm from ML.NET was employed to implement the WMB model. This algorithm is well suited for calculating the _WBI_ because it is designed for logistic regression problems and efficiently handles multi-class classification. Its ability to optimize classification tasks results in reliable predictions for different combinations of _WBI_ parameters. After the initial implementation, tests were conducted using various parameter configurations. The parameters tested included L1 and L2 regularization strengths, learning rate, epochs, and whether dropout constraints should be enforced. The selected regularization values--0.0, 0.01, 0.1, and 1.0--were chosen to thoroughly explore their effects on WBM performance for L1 and L2 regularization. This range allowed us to test configurations from no regularization (0.0) to high regularization (1.0), providing insight into feature selection and model generalization. The low values (0.01, 0.1) balanced accuracy and generalization, reducing overfitting without discarding essential features, while the highest value (1.0) tested the model's ability to handle noise and prevent complexity. We selected L1 and L2 regularization values (0.01 each) based on empirical testing, covering a range from minimal to higher regularization levels to evaluate the impact on model generalization and feature selection systematically. The results of our extensive testing across 216 configurations indicated that a low regularization setting (0.01) optimally balanced model accuracy and minimized overfitting while preserving generalization capabilities. The chosen values provided a model for well-being predictions that rely on a nuanced balance between physiological and behavioral data components. The specific values for each parameter were chosen to cover a broad range of potential model behaviors, ensuring that regularization and optimization were finely tuned. In total, 216 test configurations were derived from combinations of these parameters. Each parameter was varied to evaluate its impact on model performance. The parameters and their respective value ranges are shown in Table 1.  
Testing these parameter ranges was intended to explore the impact of regularization, learning rate, and memory usage on the model's performance. This comprehensive tuning ensured that the most suitable configuration was identified for the _WBI_ calculation. The best performance in the WBM was achieved with the configuration where L1 regularization was set to 0.01 (indicating a low L1 penalty), L2 regularization was set to 0.01 (applying a low L2 penalty), the learning rate was set to \(1\times 10^{-7}\) (ensuring high precision during optimization), the epochs were configured to 20, and the enforcement of dropout was set to false (permitting negative coefficients). The results obtained for the WBM performance indicate a _MacroAccuracy_ of 0.9181 (91.81%). The WBM demonstrates a strong ability to perform consistently across all classes, even under class imbalance. This ability ensures that minority classes are not neglected, and the model performs well overall, regardless of the frequency of each class. Achieving a 91.81% _MacroAccuracy_ demonstrates that the WBM handles different class distributions effectively. The _MicroAccuracy_ of 0.9595 (95.95%) underscores the model's excellent performance when considering the total number of correct predictions across all classes. Since _MicroAccuracy_ weighs each instance equally, this score reflects the WBM's accurate prediction of most cases. A score nearing 95.95% indicates that the model is highly reliable and provides accurate predictions on a large scale, particularly for more frequent classes. To evaluate the WBM, we used a cross-validation approach with stratified sampling to ensure a balanced representation across classes. The resulting model achieved a _MacroAccuracy_ of 91.81% and a _MicroAccuracy_ of 95.95%. Stratified cross-validation was particularly beneficial here, facilitating a more nuanced performance assessment.