## 4 Experimental Results
### Regression Model Benchmarking  
The process of selecting the regression algorithm for this study was conducted in two stages, ensuring a meticulous approach to determining the most suitable model. In the first stage, we performed a comprehensive test using multiple regression models. The results of this test were compiled and are presented in Table 6. The selection of the examination forecasting model was driven by several factors. Firstly, the models were evaluated based on their ability to accurately estimate visual comfort in indoor environments, as reflected by metrics such as the RMSE. The goal was to select a model that demonstrated the highest accuracy in predicting visual comfort levels. Additionally, temporal efficiency was taken into consideration to ensure that the chosen model could deliver results in a timely manner, avoiding excessive computational resources and processing times. Almost all types of regression model were tested to examine their differences and performance in estimating visual comfort in indoor environments. This wide-ranging analysis ensured that the selection process was thorough and encompassed a diverse range of modeling techniques. The models were assessed based on their ability to minimize the RMSE, which indicated the extent of deviation between the predicted visual comfort values and the actual values. A lower RMSE signified a higher accuracy in predicting visual comfort levels. In addition to accuracy, temporal efficiency was a vital consideration. The models' computational efficiency and processing times were carefully evaluated. It is essential to choose a model that can provide prompt results, especially in practical applications where real-time visual comfort assessment is desired. By selecting a model that achieved a balance between accuracy and temporal efficiency, we aimed to ensure the practicality and feasibility of our approach. Considering the wide range of regression models tested, this study conducted a comprehensive exploration of the available options. By thoroughly examining and comparing the performance of various models, we were able to make an informed decision and select the most suitable regression algorithm for our study. This rigorous evaluation process enhanced the reliability and credibility of the chosen model, providing a robust foundation for accurate visual comfort estimation in indoor environments. Non-parametric methods, such as decision tree regression, seemed to outperform the linear regression models in the studied scenario. Firstly, the non-parametric methods may have captured the non-linear relationships within the data that the linear regression methods may not have been able to handle. Secondly, the non-parametric methods provided greater flexibility in capturing complex relationships and interactions between variables, as they were not bound by assumptions of linearity or specific functional forms. Thirdly, they tended to be more robust to outliers, which could heavily impact the estimated coefficients in linear regression. Lastly, the non-parametric methods naturally captured the variable interactions without explicitly specifying the interaction terms, allowing for more accurate predictions compared to linear regression, which assumed additive relationships between predictors. \begin{table}
\begin{tabular}{l l} \hline
**Evaluation Metric** & **Equation** \\ \hline Mean squared error (MSE) & MSE = \((\frac{1}{n})\sum_{i=1}^{N}(R_{i}-P_{i})^{2}\) \\ \hline Root mean squared error (RMSE) & RMSE = \(\sqrt{(\frac{1}{n})\sum_{i=1}^{N}(R_{i}-P_{i})^{2}}\) \\ \hline Coefficient of determination (R2 score) & \(1-\frac{\text{SS}_{i}\text{e}\text{s}}{\text{S}_{i}\text{o}\text{t}}=1-\frac{ \sum_{i=1}^{n}(P_{i}-R_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-y)^{2}}\) \\ \hline Execution time & Sec. \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of evaluation metrics. After careful evaluation, the DecisionTreeRegressor model emerged as the optimal choice due to its balance between accuracy and time efficiency.