## 3 Results
The performance of the six selected models slightly increase as the regulated parameters gradually increased; therefore, optimizing these hyperparameters can drastically improve the ML model's performance. Figure 4 illustrates the simulation results of the selected ML models with default and tuned parameters in terms of the four evaluation metrics. There is a modest improvement in the performance of all models after using the hyperparameters identified by the Bayesian optimization tuning technique. For instance, under the default hyperparameter values, the ANN classifier scores an _ACC_ of 93%, a _PD_ of 93.4%, a _Pmd_ of 6.6%, and a _Pfa_ of 3.37%. This classifier achieves an _ACC_ of 99.3%, a _PD_ of 99.4%, a _Pfa_ of 2.01%, and a _Pmd_ of 0.6% with the tuned values. Similarly, when the RF classifier used the default parameters, it achieves an _ACC_ of 95.23%, a _PD_ of 96%, a _Pfa_ of 3.2%, and a _Pmd_ of 4%. The model achieves an _ACC_ of 99.43%, a _Pmd_ of 99.4%, a _Pmd_ of 1.01%, and a _Pmd_ of 1.01%, respectively. Figure 5: Results of feature correlation and feature importance. (**a**) Heatmap of Pearson’s Correlation Coefficient technique; (**b**) Chi-Square Feature Importance. Figure 6: Impact of data correlation on model performance. ACC_ of 99.89%, a _PD_ of 99.87%, a _Pfa_ of 1.8%, and a _Pmd_ of 1.3% when using the tuned hyperparameters. The GNB classifier yields an _ACC_ of 88%, a _PD_ of 84.44%, a _Pmd_ of 15.56%, and a _Pfa_ of 5.8%. The same classifier yields an _ACC_ of 91%, a _PD_ of 86.16%, a _Pmd_ of 13.84%, and a _Pfa_ of 5.8% with tuned parameters. The CART classifier has an _ACC_ of 97%, a _PD_ of 98.1%, a _Pfa_ of 4.6%, and a _Pmd_ of 1.9% with the default parameters, while the same classifier using the tuned parameters yields an _ACC_ of 99.99%, a _PD_ of 99.98%, a _Pfa_ of 1.1%, and a _Pmd_ of 0.02%. The same observations hold true for the LR classifier. This classifier yields an _ACC_ of 89.6%, a _PD_ of 84.4%, a _Pmd_ of 15.6%, and a _Pfa_ of 3.3% with the default parameters, and an _ACC_ of 91%, a _PD_ of 86.19%, a _Pmd_ of 13.81%, and a _Pfa_ of 3% with tuned parameters. Figure 7: Impact of training set size on model performance. (**A**) Without correlation; (**B**) With correlation. Figure 5 depicts the heatmap for the Pearson's Correlation Coefficient and Chi-squared feature importance results. Figure 5a indicates that five pairs of features, DO and TCD, TOW and RX, PC and LC, PC and EC, and EC and LC, are highly correlated with coefficient values greater than 0.9. Figure 5b illustrates that the importance of TCD is greater than DO, RX is greater than TOW, LC is greater than PC, EC is greater than PC, and LC is greater than EC; therefore, we can keep the features with higher importance scores and remove those with lower scores. We discarded TOW, PC, and DO from the given dataset and conducted the training, testing, and validation of all models with the remaining nine features: PRN, PD, RX, PIP, PQP, TCD, C/N0, CP, and LC. Figure 6 depicts the simulation results of the models' performance with and without correlated features. All models with correlated features yield better results in terms of the four metrics. For instance, the models have lower \(ACC\) with the three correlated features than those without correlation. The LR classifier yields the highest difference between correlations and without correlations. This classifier has a 5.23% lower \(ACC\) without correlated features. The SVM model also achieves a 2.39% lower \(ACC\) without correlation. Other models, such as ANN, RF, GNB, and CART, have a lower \(ACC\) without correlations. The CART model exhibits the lowest difference between model \(ACC\) in terms of with and without correlation, with a 0.05% lower accuracy, while the ANN, RF, and GNB has approximately the same difference, 0.6%, after removing correlations from the given dataset. The \(PD\) exhibits the same trends.