## 3 SDVN Traffic Analysis and Prediction
The following further uses the confusion matrix to compare the classification results of the algorithm with the actual measured values. In the domain of machine learning, the confusion matrix is also referred to as the probability matrix or error matrix. It serves as a fundamental tool to evaluate the accuracy of classification algorithms by systematically summarizing prediction outcomes. Structurally, each column in the matrix represents the predicted class, with the column total indicating the number of data instances classified into that predicted category. Conversely, each row corresponds to the true class of the data, where the row total denotes the actual number of instances belonging to that class. The cell values at the intersection of rows and columns specify how many true class instances were predicted into each respective class [33]. The confusion matrix comprises four key classification outcomes: True positive (TP): Occurs when the sample's actual class is positive, and the model correctly identifies it as positive. False negative (FN): Happens when the sample belongs to the positive class, but the model misclassifies it as negative. False positive (FP): Arises when the sample's true class is negative, yet the model incorrectly labels it as positive. True negative (TN): Represents the case where the sample's real class is negative, and the model accurately predicts it as negative. The real category of the sample is negative, and the model identifies it as negative. Since the classification accuracy of the support vector machine algorithm is the worst, the following further analyzes the confusion matrix of the other five algorithms except the support vector machine, as shown in Figure 10:  
Figure 10a-e correspond to the confusion matrix of the XGBoost classification algorithm, random forest algorithm, logistic regression algorithm, integration algorithm, and sequential model neural network algorithm, respectively. From the perspective of method type comparison, the left side of Figure 10 corresponds to a machine learning algorithm. Figure 10: Confusion Matrix of the Mentioned Algorithms. The right side corresponds to a deep learning algorithm. Taking the confusion matrix of the sequential model neural network algorithm as an example, 107 in the first column of the first row indicates that 107 instances of the actual predicted 'low' traffic are correctly predicted as 'low' traffic. In the second column of the first row, 11 instances that actually belong to 'low' traffic are incorrectly predicted as 'high' traffic; the 18 in the first column of the second row indicates that the 18 instances that actually belong to the 'high' flow are predicted to be 'low' flow; and the 64 in the second column of the second row indicates that the 64 instances that actually belong to 'high' traffic are correctly predicted as 'high' traffic. Similarly, with reference to the above analysis methods, the true positive, false negative, false positive, and true negative indexes of XGBoost classification algorithm, random forest algorithm, logistic regression algorithm, and integrated algorithm can be analyzed from the confusion matrix. In addition, more advanced classification indicators can be obtained from the confusion matrix: precision and recall. Precision is the most commonly used classification performance index. It can be used to represent the accuracy of the model, that is, the correct number/total number of samples identified by the model. Generally speaking, the higher the accuracy of the model, the better the effect of the model. The specific definition is as shown in Formula (13):  
Figure 6:0  
In addition, the definition of recall is as follows:  
Figure 6:1  
The recall rate, also known as sensitivity, measures the classifier's ability to identify positive samples among the actual positive instances. Specifically, it is defined as the ratio of the number of positive samples accurately identified by the model to the total number of positive samples. Precision and recall are two conflicting metrics.