## 3 Results
### Single-Label Classification for Depression  
The performance results of the single-label classifier for depression using the training set of (1) only activity tracking features and (2) the activity tracking and minimal K-SGDS assessment features are shown in Tables 7 and 8, respectively. We evaluated multiple classification algorithms using the training set to find the best performing hyperparameters per algorithm. In Table 7, the best performing classifier for depression using only activity tracking features was GBC 69.6%. Since it is practically meaningful if the confidence rate is over 80% [43], only using activity tracking features is not accurate enough for a practical implementation. In Table 8, the best performing classifier for depression using the activity \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Classifier** & **Hyperparameters** \\ \hline Depression & LRC & \{’C’: [10, 100, 1000] \\  & SVC & \{’C’: [1, 10, 100], \\  & & ’gamma’: [0.01, 0.1, 1], \\  & & ’kernel’: [’rbt’, ’linear’]] \\  & RFC & \{’n_estimators’: [8, 16, 32]] \\  & GBC & \{’n_estimators’: [32, 64, 128], \\  & & ’learning_rate’: [0.8, 1.0, 1.2]] \\ \hline Anxiety & LRC & \{’C’: [0.1, 1, 10]] \\  & SVC & \{’C’: [1, 10, 100], \\  & & ’gamma’: [0.01, 0.1, 1], \\  & & ’kernel’: [’rbt’, ’linear’]] \\  & RFC & \{’n_estimators’: [32, 64, 128]] \\  & GBC & \{’n_estimators’: [4, 8, 16], \\  & & ’learning_rate’: [0.4, 0.6, 0.8]] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameters for classification using both activity tracking and minimal assessment features. tracking and minimal K-SGDS assessment features was RFC with 96.4%. Most of these algorithms for depression have an \(F_{1}\) score of 90% or higher, which is extremely accurate. Therefore, it can be justified to use the minimal assessment features in conjunction with the activity tracking features for depression. To further understand the classification characteristic of the best depression classifier (RFC), we investigated the confusion matrix of RFC, as shown in Table 9. According to the table, the FN error of 1 means that the classifier incorrectly classified one in ten people as negative for depression, and the FP errors of 0 means that there are no FP classification. The learning curves of the RFC for the depression classifier are shown in Figure 4. The graph that shows how the error changes in each case as the number of training sets increases is called a learning curve. The training score is the score when testing the model with train data, and the cross-validation score is the score when testing the test set by resampling it to estimate the general performance of the model. The training score for depression was 1.0, and the cross-validation score was about 0.85, while the training score for anxiety was 1.0, and the cross-validation score was about 0.9. There is no small gap between the training and cross-validation score curves, which indicates a high variance. It would be helpful to collect more sample data for depression. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classifier** & **Parameters** & **Accuracy** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) **Score** \\ \hline RFC & \{’n\_estimators’: 32\} & 0.6847 & 0.6887 & 0.6847 & 0.6823 \\ GBC & \{’learning\_rate’: 0.6, ’n\_estimators’: 64\} & 0.6989 & 0.7072 & 0.6989 & 0.6958 \\ SVC & \{’C’: 1000, ’gamma’: 0.01, ’kernel’: ’rbf’\} & 0.6818 & 0.6969 & 0.6818 & 0.6769 \\ LRC & \{’C’: 0.01\} & 0.5938 & 0.5965 & 0.5938 & 0.5891 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance scores of the depression classifier using only activity tracking features on the training set.