## 6 Results  
The metrics calculated fell into two categories: commonly used, and those proposed by [PERSON] et al. [17]. The metrics are described in Section 4, and their values are summarised in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **SVM** & **KNN** & \begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \
\begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} &
\begin{tabular}{c} **Neural Net ANN** \\ **Model (3)** \\ \end{tabular} \\ \hline True Positive & 144 & 147 & 143 & 152 & 153 & 146 \\ \hline True Negative & 138 & 133 & 137 & 134 & 128 & 138 \\ \hline False Positive & 2 & 7 & 3 & 6 & 12 & 2 \\ \hline False Negative & 9 & 6 & 10 & 1 & 0 & 7 \\ \hline Accuracy & 96.25\% & 95.56\% & 95.56\% & 97.61\% & 95.90\% & 96.93\% \\ \hline True Positive Rate & 94.12\% & 96.08\% & 93.46\% & 99.35\% & 100.00\% & 95.42\% \\ \hline False Positive Rate & 1.43\% & 5.00\% & 2.14\% & 4.29\% & 8.57\% & 1.43\% \\ \hline True Negative Rate & 98.57\% & 95.00\% & 97.86\% & 95.71\% & 91.43\% & 98.57\% \\ \hline False Negative Rate & 5.88\% & 3.92\% & 6.54\% & 0.65\% & 0.00\% & 4.58\% \\ \hline Precision & 98.63\% & 95.45\% & 97.95\% & 96.20\% & 92.73\% & 98.65\% \\ \hline \(F1\)-Score & 96.32\% & 93.11\% & 95.65\% & 97.75\% & 96.23\% & 97.01\% \\ \hline Area under ROC curve & 97.62\% & 98.97\% & 97.32\% & 97.70\% & 99.84\% & 97.54\% \\ \hline Positive Likelihood Ratio (1) & 65.88 & 12.99 & 43.62 & 15.45 & 11.67 & 66.8 \\ \hline Negative Likelihood Ratio (2) & \(-0.06\) & \(-0.08\) & \(-0.07\) & \(-0.01\) & 0 & \(-0.05\) \\ \hline Diagnostic Odds Ratio (3) & \(-1104\) & \(-167.82\) & \(-653.03\) & \(-2212.44\) & \(<\)div by zero\(>\) & \(-1439.14\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of metrics for different models.  
The PLR values were above ten, indicating a good ability to classify benign samples from malicious ones. Similarly, the NLR values were below 0.1, providing the same classification levels. With regard to the DOR and according to [PERSON] et al. [17], these values can range from 0 to infinity, with values above 100 indicating a good predictive power of the model. Since we have negative NLRs, these values are actually less than zero. The NND values, as was already described in Section 4, constantly evaluate to 1. Finally, the Net Benefit has been calculated at \(p\) = 0.10, analogous to investigating nine false positives for every true positive detected.  
The tendency of decision trees to overfit their training data, and be influenced by anomalous training data, has previously been documented ([PERSON], 1995) [41]. The original decision tree model exhibited overfitting. The revised decision tree model, limited to a maximum depth of eight levels, produces fewer false positives, more true negatives, and higher accuracy, precision, and area values under the ROC curve.  
Overfitting is also a common problem with neural networks. ANN Models 1 and 2 exhibited severe overfitting. A model suffering from overfitting has been unduly influenced by noise in the training data. It is easier to make predictions based on the training data, but the results are increasingly poor when making predictions on unseen data (the testing data).  
Overfitting was resolved in ANN Model 3 by using tanh as the activation function in the hidden layer and adjusting the number of training epochs. 50 Epochs were found to be optimum by repeated experimentation. The testing and training loss curves were closely aligned, which is evidence that the model is not overfitted. Other potential strategies for reducing overfitting include increasing the evaluation batch size and the number of hidden layers in the network.