# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Effect of DT Prediction
Table 3 illustrates a comparison among three selected machine learning decision tree algorithms based on six performance criteria (TP rate, FP rate, precision, recall, receiver operating characteristic (ROC), and precision-recall curve (PRC)). The performance results of decision tree algorithms (C4.5, RepTree, and random tree) are presented. Additionally, in the clustering field of machine learning, the performance results simple K means and hierarchical clustering algorithms are listed, where the final section of the table presents the performance results of stacking, where many classifiers can be combined to predict the results. Figure 7: Prediction margin of optimal alternatives. AA: actual alternative; PA: predicted alternative. The true positive (TP) rate represents the percentage of correct predictions as positive cases, and the false positive (FP) rate represents the percentage of incorrect predictions as positive cases of different selected classifiers and machine learning algorithms. Precision is the result of all correctly predicted cases divided by all cases. Recall or sensitivity is the result of correct predictions divided by the total positive cases. The area under the receiver operating characteristic curve (ROC) and area under the precision-recall curve (PRC) represent the general performance of the machine learning algorithm. While the TP rate is considered as optimal when its value is equal to 1, the optimal FP rate is reached when it is equal to zero. Moreover, the ideal value of precision and recall is 1.0, the ideal value of recall and precision is 1.0, where the worst value is 0. The area under the ROC curve represents the relationship between recall and specificity, where specificity represents the precision of truly predicted cases as negative when they are negative. Area under the PRC is where the relationship between recall and precision is estimated. Higher rates of ROC and PRC represent a better classifier. The congestion control dataset was divided into 10% of instances as a test dataset and 90% of all data as a training dataset. The experimental congestion control dataset was comprised of 13% optimal class and 87% non-optimal class. The extraordinary outcome of DT (C4.5) observed from Table 3 shows that C4.5 was the best among the selected algorithms. The optimal TP rate is 1.0, and the TP rate of DT C4.5 was near 0.924--better than both RepTree and random tree. The closer the FP rate is to zero, the more optimal the result. The C4.5 DT FP rate satisfied this condition. Precision and recall values were optimistic because they were close to the optimal value of 1.0. In respect of the extraordinary DT C4.5 performances observed in Table 3, the goal of this research to enhance the network performance by utilizing DT C4.5 was achieved. Additionally, the tree graph of the decision tree allows characterization with simplicity and ease, making it the best choice for analyzing prediction results. Furthermore, ROC was used to clarify the classifier accuracy. Some datasets may achieve a prediction of 92% of instances and result in a non-preferable classifier. The ROC curve can determine whether or not the classifier is appropriate for prediction. The nearer the curve is to 1.0, the better the classifier is. Figure 8 shows that the model was suitable and reliable for prediction. The area under the ROC curve was 0.889, which is a reasonable and optimal value.