# Predicting Team Well-Being through Face Video Analysis with AI
## 4 Results
### Data Analysis  
The prediction of the PERMA scores is approached both as a regression and as a binary classification task (classification of the PERMA score level as high or low). Thus, different models are used and their respective hyperparameters that have to be tuned for proper performance of the models. Table 3 provides a summary of the models used for the classification and the regression task, respectively. It also summarizes the various hyperparameters tuned using grid search and cross-validation on the training dataset. For each target variable \(tar\) of the PERMA survey, the training set (\(X_{train}\), \(y_{train}\), and \(y_{bin_{train}}\)) is split in \(k\)-_folds_ in order to find the best combination of hyperparameters. The chosen model is the one that has the lowest validation error or the highest performance metric, such as balanced accuracy for classification or MAE for regression. Finally, the models are trained using the training sets. A 5-fold cross-validation on the training set is used to tune the models under consideration. Each pillar of the PERMA model is analyzed independently. Tables 4 and 5 depict the regression and classification models, respectively, as well as their hyperparameters offering the best performance on the validation set. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Model** & **Best Hyperparameters** \\ \hline P & CatBoostRegressor & Iterations: 50, Learning Rate: 0.01, Depth: 4, Loss Function: RMSE \\ E & AdaBoostRegressor & Learning Rate: 0.1, N Estimators: 400 \\ R & BayesianRidge & Alpha 1: 1.0, Alpha 2: 1.0 \\ M & ElasticNet & Alpha: 0.01, L1 Ratio: 0.9 \\ A & BayesianRidge & Alpha 1: 0.001, Alpha 2: 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal hyperparameters of the regression models. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Classification** & **Regression** & **Hyperparameters** \\ \hline Gaussian Naive Bayes & - & var smoothing \\ K-Nearest Neighbors & K-Nearest Neighbors & n neighbors \\ Logistic Regression & - & C, penalty, solver, class weight \\ - & Linear Regression & - \\ Ridge Classifier & Ridge Regression & alpha, class weight \\ - & Lasso Regression & alpha \\ - & Elastic Net & alpha \\ Decision Tree & Decision Tree & max depth \\ Support Vector Machine & Support Vector Regression & kernel, C, shrinking, class weight, epsilon \\ - & BayesianRidge & alpha 1, alpha 2 \\ Random Forest & Random Forest & n estimators, max depth, class weight \\ Extra Trees & Extra Trees & n estimators, max depth, class weight \\ AdaBoost & AdaBoost & n estimators, learning rate \\ Gradient Boosting & Gradient Boosting & n estimators, max depth, learning rate \\ CatBoost & CatBoost & iteration, depth, learning rate, auto class \\ XGBost & XGBost & iteration, depth, learning rate, scale pos \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification and regression models and associated hyperparameters used in the methodology. The predominance of the CatBoostClassifier model in the classification task is evident in Table 5. This model is chosen for the classification of the level of four of PERMA's five pillars. There is no such evidence in the regression task since, as described in Table 4, each pillar is predicted by a different model, with the exception of pillars R and A, which are both predicted by the BayesianRidge model. The best models and their associated hyperparameters are trained and tested using the training and the test set, respectively. The performance on the test set of the regression models is calculated using the MAE metric to measure the mean absolute error between predicted and actual values [46]. In Figure 5, the performance of each model is compared to a baseline where the proposed precision is the average pillar value observed over the test set. The performance on the test set of the classification models is calculated using the balanced accuracy metric to encourage the model to correctly predict examples from all classes, regardless of their size [48].