## 3 Result Analysis
### Results of Thermal Models with Classification Algorithms  
Based on a 5-fold cross-validation with both female and male data, Table 7 shows the recall scores with cross-validation based on different parameter values of SVM and RF, respectively. As shown in the table, in terms of SVM model configuration, SVM with linear kernel has much better performance than Gaussian kernel with feature set A while there are few differences among all SVM configurations with the base feature set and feature set B for both subjects. Meanwhile, in terms of RF model configuration, there are few differences among all combinations of RF configurations and features sets. Since a larger penalty number will result in a slower SVM model and larger tree depth, and the number of trees will also result in a slower RF model, SVM with a linear kernel and penalty number of 1 were selected as the best model configuration SVM, and RF with maximum depth of 3 and 50 trees were selected as RF best model configuration. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Average Recall Score**} \\ \cline{2-7}
**Model Configuration** & \multicolumn{3}{c}{**Female**} & \multicolumn{3}{c}{**Male**} \\ \cline{2-7}  & **Base** & **A** & **B** & **Base** & **A** & **B** \\ \hline
**SVM, C = 1, kernel = linear** & 0.65 & 0.98 & 0.65 & 0.62 & 0.96 & 0.57 \\ SVM, C = 1, kernel = Gaussian & 0.62 & 0.68 & 0.57 & 0.61 & 0.78 & 0.59 \\ SVM, C = 10, kernel = linear & 0.62 & 0.98 & 0.6 & 0.6 & 0.96 & 0.6 \\ SVM, C = 10, kernel = Gaussian & 0.62 & 0.82 & 0.52 & 0.6 & 0.88 & 0.58 \\ \hline
**RF, Max_depth = 3, number of trees = 50** & 0.6 & 0.96 & 0.6 & 0.68 & 0.95 & 0.59 \\ RF, Max_depth = 3, number of trees = 100 & 0.6 & 0.96 & 0.59 & 0.58 & 0.96 & 0.61 \\ RF, Max_depth = 4, number of trees = 50 & 0.63 & 0.97 & 0.59 & 0.57 & 0.99 & 0.62 \\ RF, Max_depth = 4, number of trees = 100 & 0.62 & 0.99 & 0.59 & 0.6 & 0.98 & 0.6 \\ \hline \hline \end{tabular} SVM: support vector machine; RF: random forest.  
\end{table} 
Table 7: Best parameters selected with cross-validation. Figure 6: Relationships between thermal sensations and (a) skin temperature of female subject (b) clothing surface temperature of female subject (c) skin temperature of male subject (d) clothing surface temperature of male subject. Tables 8 and 9 show the performance benchmark among the thermal models of the two subjects with the three feature sets trained by SVM, RF and PMV, respectively. As a result, for the female subject, precision, recall and F1 scores of PMV on test data are 48.6%, 48.6% and 48.3%, respectively. Compared to the baseline of PMV, SVM and RF have better performances with all three different feature sets. Among the three feature sets, feature set A comprised of indoor air temperature, relative humidity, side face skin temperature and clothing surface temperature has 100% recall, precision and F1 score with SVM, which significantly outperforms the baseline. This may be because the effect of clothing insulation on thermal comfort has been taken into account since the dynamic clothing insulation can be estimated with clothing surface temperature and skin temperature [30], and the causal effect analysis in Section 3.1 also illustrates the clothing surface temperature has a higher correlation with thermal sensations than the skin temperature. In addition, SVM performs better than RF with all three feature sets. For the male subject, precision, recall and F1 score of PMV on test data is only 35%, 33% and 31.8%, respectively. Moreover, even if outperforming the baseline with the recall score, both SVM and RF have much worse performances in predicting thermal sensation of the male subject with the base feature set and the feature set B than those of female subject. This indicates individual differences with respect to thermal comfort such as gender, thus the personalized heating and cooling system being important to develop.