# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 5 Discussion  
In most cases, the personalized ML approach demonstrated a slightly stronger ability to distinguish the nuances of each subject's emotion expressions compared to the general-purpose models. In cases where an improved performance was not observed for a subject, the PCA revealed a lack of sufficient data separation for the subject's expressions with respect to the general-purpose models. These results support the effectiveness of the personalized ML approach in classifying emotions and highlight its potential for further advancements in the field of emotion recognition. While our examination focused on model personalization within the field of affective computing, this approach can be extended to various precision health tasks where a specific characteristic (e.g., predicting stress levels [9; 15; 65; 66]) needs to be recurrently predicted for an individual user. In addressing the concerns of overfitting in our emotion recognition models, we used rigorous methodologies, including cross-validation, regularization, and advanced feature engineering and selection. These steps were crucial to mitigate the traditional risks of overfitting. However, it is pertinent to note that any residual model specificity, which might be perceived as overfitting in a generic context, is actually advantageous in our personalized framework. This specificity enhances the model's ability to accurately capture and interpret the unique emotional patterns of an individual, a critical factor in the success of personalized applications. The superior performance of our personalized models, as demonstrated by higher F1 scores compared to the generalized models, underscores the effectiveness of this approach in individual-focused emotion recognition. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Data Type** & **Dataset** & **Metric** & \multicolumn{1}{c}{**Value**} \\ \hline \multirow{3}{*}{Ours-Generic} & & & & KNN & 88.50\% \\  & Pose/Facial Landmarks & & RF & 91.78\% \\ \hline \multirow{3}{*}{Ours-Personalized} & & & DNN & 80.42\% \\  & & & & RF & 92.66\% \\ \cline{1-1}  & & & & DNN & 86.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall performance of the generic/personalized models using in this paper and previous general model on the same task (emotion recognition). Although convolutional neural networks and vision transformers offer the possibility of better performance gains, we deliberately opted to use classical ML methods to prioritize the interpretability of our ML models. While these state-of-the-art models have demonstrated remarkable success in image recognition, their complex architecture often renders them as \"black-box\" models, making it challenging to interpret and understand the learned features influencing their predictions. By contrast, the automatic feature extraction we performed enabled us to inspect and comprehend the specific facial features that contribute to emotion recognition between subjects. Notably, we learned that the top facial features in the personalized models differed across subjects, highlighting the need for personalized ML. Our study, while demonstrating promise for the personalized learning of relatively subjective tasks like affective computing, contains several limitations and can therefore only be considered as a feasibility study. We evaluated our method on only 10 subjects. While this experimental paradigm can be viewed as 10 independent N = 1 studies, we hope to expand this set of experiments in future work to more and larger datasets. Both the evocation and understanding of emotional expressions play a crucial role in detecting certain types of developmental disorders. For instance, ASD affects almost 1 in 44 people in America [67], and it is the fastest-growing developmental disorder in the United States [68; 69]. ASD is a multifaceted neuropsychiatric disorder that appears in diverse phenotypic forms.