## 3 Results
### The Sensitivity Analysis of SVM, RF, CART  
The above analysis demonstrates that the algorithm parameters are crucial for the classification performance of the three machine-learning algorithms. In order to further research the sensitivity of SVM, RF, and CART, this study employed the grid search method to iterate (C and gamma), (tree and split), and (maxD and minSP), respectively, and analyzed the effects of these parameter changes on the algorithm accuracy scores and classification residuals. We set the parameters {C: (102\(\sim\)103), gamma: (102\(\sim\)103), {tree: 100\(\sim\)600, split: 1-6}, {maxD: 10-60, minSP: 10-60}, {19; 22; 48}. SVM, RF, and CART were iterated with the corresponding algorithm parameters to obtain the accuracy scores under the different parameters (Figure 6(a),c,e) and classification residual (Figure 6(b),d,f). By analyzing the sensitivity of SVM toward (C and gamma) (Figure 6(a),b), it can be seen that as the values of (C and gamma) gradually increase, the accuracy score increases, and the residuals decrease. When the values of (C and gamma) are less than (1 and 1), the accuracy scores are small. In particular, when the (C and gamma) values are (0.01 and 0.01), (0.01 and 0.1), (0.01 and 1000), and (0.1 and 0.01), the accuracy scores all are 0.323, which are the smallest scores. In Figure 6(b), as the (C and gamma) increase gradually, the residual values decrease accordingly. When the values of (C and gamma) are (0.01 and 0.01), (0.01 and 0.1), (0.01 and 1000), and (0.1 and 0.01), the maximum residuals are 76,700 km\({}^{2}\). When the values of (C and gamma) are (1000 and 1000), the residuals are 1540 km\({}^{2}\). When parameter C is small, the penalty for error is relatively large [19; 48], which results in the phenomenon of underfitting, leading to large classification residuals. With the increase in gamma values, although the decision boundary becomes smoother and individual features can be better fitted, overfitting may also occur [48]. When (C and gamma) are (1 and 100), the accuracy score is 0.95. In general, when the values of (C and gamma) are small, the error penalty for the classified data is larger, and the accuracy scores are generally smaller. As the values of (C and gamma) increase, so does the accuracy score. When the values of (C and gamma) exceed (1 and 1), the accuracy score gradually tends to be stable. In general, SVM is sensitive to parameters (C and gamma). In Figure 6(c),d, RF is not sensitive to the parameters (tree and split); the smallest accuracy score of (tree and split) is 0.932, while the largest score is 0.940, with a small variation. In Figure 6(d), the maximum value is 2859 km\({}^{2}\) and the minimum value is 1702 km\({}^{2}\), and the variation range of the residuals is relatively small. RF is an assemble algorithm based on a decision tree, which integrates multiple weak classifiers into a strong classifier. Multiple weak classifiers vote on the final classification result to obtain the optimal result [9; 50]. Therefore, the classification results of RF are generally stable when sample data are sufficient. In this study, under the control of parameters (tree and split), the number of trees have a small impact on RF, while splits have a large impact. The split determines the number of features required to split; when the value of the split is small, each tree grows deeper and overfitting occurs easily. Therefore, controlling the split value is conducive to improving the classification performance of RF. Compared to RF, CART is more sensitive to changes in the algorithm parameters (maxD and minSP). In Figure 7e, the accuracy score decreases gradually as (maxD and minSP) increase slightly. The maximum accuracy score is 0.903 and the minimum is 0.841. Correspondingly, in Figure 7f, we can see that as (maxD and minSP) increase, the residuals also increase. This indicates that changes in the algorithm parameters (maxD and minSP) directly affect the classification performance of CART.