## 4 Experimentation
### Results
It becomes less biased toward the majority class and demonstrates improved performance in recognizing minority classes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Rank** \\ \hline \(M_{E}\) & 1.0 \\ \(M_{DR}\) (all-distilroberta-v1) & 2.5 \\ \(M_{DR}\) (all-mpnet-base-v2) & 2.5 \\ \(M_{LF}\) & 4.0 \\ MentalBERT & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [PERSON] rank for the top-5 methods. Additionally, Table 6 presents a comprehensive analysis of model performances regarding the two undersampling strategies. It also showcases F-scores, precision, and recall, along with their standard deviations. It can be appreciated that undersampling leads to a decrease in average performances that can be attributed to the loss of valuable information derived from the reduction in dataset size. This decrease is higher in the DsD dataset, as the information loss is higher in this dataset given the limited number of samples in the minority class. However, the standard deviation of the performances for each class also decreases, suggesting a more consistent performance. These findings emphasize the importance of addressing class imbalance when working with the considered datasets. To conclude, we compare the results of our proposed approach with the best-performing methods found in the literature for each dataset. Table 7 shows the outcomes of this comparison. Regarding the DsD dataset, one of the most prominent approaches is the one proposed by [PERSON] et al. [20]. The authors in this work inject extra-linguistic information into transformer-based models and employ a multimodal adaptation gate for creating the combined embeddings, which are then fed to a MentalBERT model. Applying label smoothing, they yield a maximum weighted F1-score of 73.16%, which is slightly lower \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**DsD**} & \multicolumn{3}{c}{**DepSign**} \\ \cline{3-8}
**Undersampling** & **Model** & **F** & **P** & **R** & **F** & **P** & **R** \\ \hline \multirow{3}{*}{No} & \(M_{LF}\) & 0.72 \(\pm\) 0.23 & 0.72 \(\pm\) 0.22 & 0.72 \(\pm\) 0.24 & 0.61 \(\pm\) 0.13 & 0.61 \(\pm\) 0.06 & 0.62 \(\pm\) 0.19 \\  & \(M_{DR}\) & 0.72 \(\pm\) 0.22 & 0.72 \(\pm\) 0.21 & 0.72 \(\pm\) 0.22 & 0.63 \(\pm\) 0.09 & 0.64 \(\pm\) 0.07 & 0.63 \(\pm\) 0.11 \\  & \(M_{E}\) & 0.74 \(\pm\) 0.21 & 0.75 \(\pm\) 0.21 & 0.74 \(\pm\) 0.20 & 0.64 \(\pm\) 0.08 & 0.64 \(\pm\) 0.06 & 0.64 \(\pm\) 0.10 \\ \hline \multirow{3}{*}{Yes} & \(M_{LF}\) & 0.48 \(\pm\) 0.07 & 0.49 \(\pm\) 0.03 & 0.49 \(\pm\) 0.14 & 0.52 \(\pm\) 0.11 & 0.53 \(\pm\) 0.04 & 0.54 \(\pm\) 0.18 \\  & \(M_{R}\) & 0.48 \(\pm\) 0.10 & 0.49 \(\pm\) 0.02 & 0.49 \(\pm\) 0.18 & 0.59 \(\pm\) 0.09 & 0.60 \(\pm\) 0.04 & 0.60 \(\pm\) 0.15 \\  & \(M_{E}\) & 0.55 \(\pm\) 0.05 & 0.56 \(\pm\) 0.01 & 0.56 \(\pm\) 0.10 & 0.61 \(\pm\) 0.09 & 0.61 \(\pm\) 0.05 & 0.62 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Complete performances (F1-score, precision, and recall averaged and with standard deviations for each class) obtained by each model for each dataset. Figure 5: Normalized confusion matrices illustrating the performance of the \(M_{E}\) model. than the outcome obtained by our approach. For the DepSign dataset, [PERSON] et al. [32] proposed an ensemble of various fine-tuned transformer-based models, achieving a macro F1-score of 63.70%. To compare, we have computed the macro average F1-score for our approach using the splits described in their article. The results reveal that our method does not surpass the performance of the transformers ensemble model. A closer examination of these results, in conjunction with the class distribution of the data and Figure 5, suggests that our approach exhibits higher sensitivity to the class imbalance of the dataset. However, a compelling advantage of our method lies in its simplicity, making it less susceptible to reduced data availability.