## 3 SDVN Traffic Analysis and Prediction
Further, the four commonly used machine learning classifiers XGBoost classifier, Random Forest classifier, Logistic Regression, and Support Vector Machine (SVM) are used to classify the traffic level of the training set data. The training process is shown in Figure 8:  
Figure 8a-d correspond to XGBoost classifier algorithm, random forest algorithm, logistic regression algorithm, and support vector machine, in turn. After a lot of parameter adjustment and experimental comparison, one of the best initial parameters is selected for each algorithm. Due to the characteristics of the machine learning algorithm, it is impossible to guarantee that the parameter is the best for each training. But it can be as  
Figure 8: Iterative process of four machine learning classifiers. Figure 7: The sequential model loss function value and accuracy rate change with the number of iterations. close to the best as possible. The parameter settings of each machine learning algorithm are as follows. XGboost classifier algorithm parameter settings: the learning rate = 0.3, the minimum leaf node sample weight sum = 1, the maximum depth = 6, the maximum number of leaves on the tree = 6, and the remaining parameter settings are default. Random forest algorithm: the number of decision trees = 200, there is a back sampling, there is no limit on the maximum number of leaf nodes, and the remaining parameters are set to default. Logistic regression algorithm: Solver = quasi-Newton method, the maximum number of iterations = 500, and the remaining parameters are default. Support vector machine algorithm: classification error penalty coefficient = 1, random seed = 20, kernel function = Radial Basis Function (RBF), the rest of the parameters are set as default. The accuracy and standard deviation of the above four machine learning algorithms and deep learning algorithms (sequential model neural network) are summarized and compared. And the integrated model is further designed, as shown in Figure 9:  
As shown in Figure 9, the five algorithms from left to right correspond to XGBoost algorithm, random forest, logistic regression, support vector machine, and sequential model neural network. For the XGBoost algorithm, the average accuracy is 0.89, and the standard deviation is \(+/-0.07\). The average accuracy of the random forest algorithm is 0.89, and the standard deviation is \(+/-0.07\); the average accuracy of logistic regression is 0.89, and the standard deviation is \(+/-0.08\). The average accuracy of the support vector machine algorithm is 0.63, and the standard deviation is \(+/-0.01\); the accuracy of the sequential model neural network algorithm is 0.77, and the standard deviation is \(+/-0.16\). Among them, the classification accuracy of the support vector machine (SVM) algorithm is the worst, and the average accuracy is 0.63. Therefore, combining the accuracy of the above four machine learning algorithms and the sequential model neural network algorithm, it was decided to remove the support vector machine algorithm and design an ensemble learning algorithm model (ensemble model). The ensemble algorithm integrates XGBoost classification algorithm, random forest, logistic regression, and sequential model neural network to implement an ensemble classifier using weighted majority voting technology to improve accuracy. The voting weight of XGBoost algorithm, random forest, logistic regression and sequential model neural network is set as 1:1:1:0.8. The rightmost of Figure 9  
Figure 9: Accuracy box plot for six algorithms. is the box plot of the integrated algorithm, with an average accuracy of 0.90 and a standard deviation of \(+/-0.07\). According to the research of [32], they believe that the current traffic prediction method based on deep learning has better performance than the traditional prediction method. However, in this section, the performance of the machine learning algorithm is better than that of the deep learning neural network algorithm.