## 3 Results
The CART model has low sensitivity. Figure 8 shows the specificity values distribution of all models. According to the table, all models have high specificity values at all land value estimation levels. Specifically, the RF model finds a value equal to 1 at all land value estimation levels. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \hline \multirow{4}{*}{Sensitivity} & CART & 0.902 & 0.907 & 0.607 & 0.897 & 0.760 & 0.506 & 0.972 & 0.940 & 0.569 & 0.668 \\  & RF & 0.992 & 0.978 & 0.987 & 0.997 & 0.963 & 0.960 & 0.987 & 0.992 & 0.974 & 0.958 \\  & SVM & 0.989 & 0.987 & 0.944 & 0.997 & 0.918 & 0.854 & 0.997 & 1.000 & 0.937 & 0.914 \\ \hline \multirow{4}{*}{Specificity} & CART & 0.994 & 0.943 & 0.937 & 0.915 & 0.987 & 0.980 & 0.941 & 0.961 & 0.970 & 0.964 \\  & RF & 0.998 & 0.997 & 0.992 & 0.997 & 0.998 & 0.998 & 0.998 & 0.997 & 0.995 & 0.996 \\  & SVM & 0.994 & 0.991 & 0.977 & 0.992 & 0.997 & 0.999 & 0.993 & 0.971 & 0.991 & 0.993 \\ \hline \multirow{2}{*}{Pos Pred} & CART & 0.815 & 0.214 & 0.416 & 0.466 & 0.823 & 0.963 & 0.438 & 0.315 & 0.642 & 0.620 \\  & RF & 0.932 & 0.853 & 0.902 & 0.960 & 0.969 & 0.998 & 0.951 & 0.873 & 0.952 & 0.956 \\  & SVM & 0.813 & 0.654 & 0.754 & 0.910 & 0.956 & 0.999 & 0.868 & 0.393 & 0.904 & 0.921 \\ \hline \multirow{2}{*}{Neg Pred} & CART & 0.997 & 0.998 & 0.970 & 0.991 & 0.982 & 0.656 & 0.999 & 0.999 & 0.960 & 0.970 \\  & RF & 1.000 & 1.000 & 0.999 & 1.000 & 0.997 & 0.960 & 0.999 & 1.000 & 0.998 & 0.996 \\ \hline \multirow{2}{*}{Value} & CART & 1.000 & 1.000 & 0.996 & 1.000 & 0.994 & 0.868 & 1.000 & 1.000 & 0.994 & 0.992 \\ \hline \hline \end{tabular} Note: THB/wah\({}^{2}\) equal THB / 4 m\({}^{2}\).  
\end{table} 
Table 8: Model development in the testing area. Figure 7: Sensitivity values distribution of all models. Figure 9 shows the positive prediction values distribution of all models. At all land value estimation levels, RF and SVM models were found to have high positive prediction values. Except those at levels 15,001-18,000 THB/Wah2 and 50,001-80,000 THB/Wah2, SVM was noticeably less than the RF model, while with the CART model, positive prediction values were poor. Figure 10 shows the negative prediction value distribution of all models. In the negative prediction value, all models had high negative prediction values. Specifically, RF found a value equal to 1 at almost every land value distribution. Except for 32,001-40,000 THB/Wah2. Figure 8: Specificity values distribution of all models. Figure 9: The positive prediction values distribution of all models. [PERSON] [39] claims that MAPE values of less than 10% indicate a high level of accuracy, while values of 10% to 20% indicate a reasonable level of accuracy. Thus, RF (1.906) and SVM (5.242) are a high level of accuracy. At the same time, CART (20.391) is a reasonable level of accuracy. Table 9 shows the average MAPE value of machine learning models. After all models have been validated, each model's predicted probabilities of land value estimation can be removed in a data frame format. Then, the testing feature class and predicted probabilities data frame are joined together using featured ID field by GIS programming. After successful joining, the estimated land value map is created. According to the estimation land value, using points-of-interest data and the machine learning classification algorithms (Figure 11), the RF (Figure 11C) and SVM (Figure 11D) maps are the closest to the testing map. Meanwhile, the CART (Figure 11B) map was less accurate than the testing map (Figure 11A).