## 5 Experimental Results
### Comparison of Outcomes  
In this paper, the dataset had sixteen attributes, and the class prediction was based on whether or not the packets were received, with a YES or NO, represented by Y/N. The training dataset's number of instances was 10,000, and the test dataset's number of instances was 6346. In order to compare and demonstrate the performance of non-parametric and parametric classifiers, an ideal choice of logistic regression was made, as the dependent/predictor variable was categorical. The outcome of classes receiving the packet was categorized with Y/N. Complete accuracy measures of parametric (logistic regression) and non-parametric classifiers (SVM types) are listed below in Table 5. The average accuracy percentage of logistic regression (LR), a parametric classifier, was 66%, while LIBLINEAR was below 65%. LIBSVM seemed to be much better than LIBLINEAR and LR (parametric classifier); however, the SVM with an RBF kernel under a grid search was a well-refined and optimal package, trained to solve quadratic programming problems while ensuring an accuracy percentage of over 99%. A comparison of the results is provided in Table 5.  
This illustrates that when the cross-validation method was applied to the selected SVMs and logistic regression [18; 19; 20; 21], the accuracy of LIBSVM and LIBLINEAR improved significantly more than that of the SVM-RBF. The reason for this was that they were run over a simple probability, in an effort to further optimize and enhance performance, by tuning the cost (C) parameter. When cross-validation was applied to them, the performance elevated and also resulted in bad generalization/overfitting. Figure 3: Training and testing architecture. The SVM-RBF was already a fine-tuned technique, embedded with the advanced probability techniques of SVMs. This technique jointly optimized cost and gamma parameters. The SVM-RBF guaranteed a consistently good generalization with no overfitting curves when the variance was averaged by the cross-validation method. The accuracy of the dataset for lap five of the northbound traffic is showcased by class in Table 6, where Y indicates that the packets were received, while N denotes that the packets were not received. The dataset had 16 features/attributes and 6346 instances upon which the test run was done, to showcase the compared performance measures of LIBLINEAR, LIBSVM, and the SVM-RBF. TP reflects the true positive rate, while FP reflects the false positive rate. Receiver operating characteristics (ROCs) had an area ranging from zero to one. When the ROC area [22] value was less than 0.500, the prediction was labeled as random/poor. On the other hand, it was considered a good, excellent, or perfect prediction as the value approached 1.000. Precision and recall were computed to confirm the accuracy rate with \(F\)-measure, which expressed the stability between them. Precision-recall curves (PRCs) highlighted the differences between models. Finally, [PERSON]'s correlation coefficient (MCC) was used as a geometric mean, which measured the quality of classification. All accuracy measures are explicitly represented in Figure 4, which showcases that the accuracy rating of the SVM-RBF outperformed the logistic regression classifier and other SVMs.