## 4 Discussion  
Machine learning algorithms are widely used in agricultural fields such as land use/cover, crop identification and mapping, etc. However, the effect of parameters setting on their classification performance has not received enough attention. Many works have shown that when SVM is used for classification, the selection of kernel function and the value of parameters will have an important impact on the classification performance [33; 48; 62; 63]. The small parameter C tends to emphasize hyperplane margin while ignoring outliers, while the larger parameter C tends to overfitting easily [49]. Parameter gamma control kernel width, and large values of gamma also lead to overfitting. For tree-based machine learning algorithms, RF and CART, the algorithm parameters also have an important impact on the classification performance. RF is classification algorithms that assembles many decision trees. Each tree votes the classification results and selects the result with the most votes as the final classification result. Therefore, RF shows more stable classification results and stronger robustness in many classification studies [64; 65]. Generally, the number of trees affects the accuracy of RF classification. When the tree value is large, the classification results tend to overfitting. In the study, other RF parameters (spit) also affect the classification performance, but it is usually ignored in research. CART is a single decision tree algorithm, which divides the data features into binary recursion, and finally obtains the classification results. Similarly, the setting of algorithm parameters (maxD, minSP) is easy to ignore. How to determine the appropriate C and gamma of SVM is not a problem that cannot be ignored, and also an important basis for correct research conclusions. This paper comprehensively discussed the classification mechanism of SVM, RF and CART, and explored the performance of SVM, RF, and CART to variations of the algorithm parameters. We found that the classification accuracy of SVM is better than that of RF and CART. This is because SVM has a unique classification advantage in the face of multiple features, and previous studies have also confirmed this point. However, SVM is sensitive to the change of algorithm parameters. Different C and gamma values affect the accuracy scores and residuals of the algorithm. Generally, If the C and gamma are small, the model tends to be underfitting, while if it is large, it tends to overfitting. In addition, for RF and CART algorithm, the classification performance of RF is better than CART, because a multi-trees ensemble algorithm has stronger robustness than a single decision tree. This point is also consistent with previous studies. However, RF is less sensitive to parameter changes. Under the joint control of tree and split, the effect of split's values on RF is greater than tree's values. This is because the final classification effect of RF depends on each tree, while the growth of a single tree is affected by the values of split. For CART algorithm, we found that it is also sensitive to the change of algorithm parameters. Moreover, under the control of parameters (minD, minSP), the values of minSP have a greater impact on the performance of CART. In conclusion, this study found that the classification performance of the three algorithms were affected by the algorithm parameters, although different algorithms have different effects. This further shows that it is necessary to pay attention to the effect of algorithm parameters when using a machine-learning algorithm to perform classification research. Generally, the default parameter value does not help to improve the accuracy of algorithms, so the optimal result can be obtained by tuning parameters properly. There are still some uncertainties in this study, which can be further explored in the future. Cross validation is widely used in machine-learning algorithms, and its advantage is to evaluate the classification performance of prediction models for unknown data.