# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 4 Results  
We conducted a comprehensive evaluation of the models, including the KNN classifier, RF, and DNN, using a rigorous procedure involving component setup, cross-validation, and performance assessment. In the personalized experiment, the KNN model achieved an average F1 score of 0.904, while in the generic experiment, it attained an average F1 score of 0.885. Similarly, the RF yielded average F1 scores of 0.926 and 0.917 in the personalized and generic experiments, respectively, while the MLP classifier obtained average F1 scores of 0.864 and 0.804 for the personalized and generic experiments, respectively. We directly compare the F1 score of the personalized vs. generic models for all 10 subjects (Table 2). The personalized ML approach outperformed the general-purpose models for emotion recognition in 7 out of the 10 individuals. Analyzing the ROC curves and confusion matrices for a demonstrative subject (no. 22) provides insights into the personalized models' classification capabilities (Figure 4). Retaining the best hyperparameter combinations aided in identifying the optimal settings and understanding the model's behavior. In certain instances, the performance of the personalized models did not surpass that of the generic models (Table 2). To investigate this discrepancy, we analyzed the PCA plots for these individuals. The PCA plots revealed that the data points representing the emotional states did not form distinct clusters for these subjects, especially with respect to the separation of the generalized dataset containing data from all 10 subjects (e.g., Figure 5). Consequently, it is foreseeable that personalized models might encounter difficulty in accurately discerning individual emotional states compared to generic models in cases where the individual makes relatively little variation in their facial movement across emotions. The enhanced performance of generalized models in cases where subjects show little variation in emotional expressions can be attributed to their training on a more diverse range of data, which helps in better recognizing subtle emotional differences. Personalized models may struggle with these subtleties due to overfitting to specific, non-distinct features. Additionally, generalized models, being less sensitive to individual variability and noise in the data, can more effectively handle such minimal expression differences. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**F1 Score (Personalized Models)**} & \multicolumn{3}{c}{**F1 Score (Generic Models)**} \\ \cline{2-7}  & **KNN** & **RF** & **DNN** & **KNN** & **RF** & **DNN** \\ \hline No. 22 & 93.1\% & 95.3\% & 88.2\% & 86.9\% & 91.4\% & 76.7\% \\ \hline No. 25 & 89.3\% & 91.5\% & 83.4\% & 88.0\% & 92.0\% & 81.2\% \\ \hline No. 28 & 96.6\% & 97.8\% & 93.7\% & 88.5\% & 92.7\% & 82.9\% \\ \hline No. 29 & 83.6\% & 86.3\% & 78.6\% & 90.0\% & 92.0\% & 84.4\% \\ \hline No. 32 & 93.4\% & 95.1\% & 91.8\% & 92.6\% & 95.0\% & 88.0\% \\ \hline No. 39 & 87.2\% & 90.0\% & 83.8\% & 92.1\% & 94.0\% & 87.0\% \\ \hline No. 40 & 99.6\% & 99.9\% & 97.2\% & 88.3\% & 91.0\% & 78.4\% \\ \hline No. 42 & 93.2\% & 94.7\% & 89.9\% & 87.2\% & 91.6\% & 78.0\% \\ \hline No. 45 & 82.5\% & 86.3\% & 73.3\% & 82.2\% & 87.1\% & 63.9\% \\ \hline No. 48 & 86.3\% & 89.7\% & 84.1\% & 89.7\% & 91.0\% & 83.7\% \\ \hline \end{tabular}
\end{table} 
Table 2: Overall performance (F1 score) of the models in both personalized and generic approaches on the same evaluation task. Subjects whose performance in the personalized model was lower than for the generic model are highlighted in red. Figure 4: Confusion matrices and ROC curves for 3 separate models, **(a)** KNN, **(b)** RF, and **(c)** DNN, each trained and evaluated on a demonstrative subject, no. 22. Figure 5: PCA plot for subject no. 39, contrasting the results in a personalized dataset (**a**) with those in a generalized dataset (**b**).