## 4 Evaluation
### Metrics  
The following metrics were calculated according to the Equation set out by [PERSON] et al. (2020). The _Likelihood Ratio (LR)_ was used to indicate how well the model distinguishes between benign and malicious samples. Two Equations were given for LR--_Positive Likelihood Ratio (PLR)_ (Equation (4a)) and _Negative Likelihood Ration (NLR)_ (Equation (4b)). PLR indicates how well the model distinguishes benign samples from malicious ones. A value of 10 or more shows that the model distinguishes well. An NLR of less than 0.1 also indicates that the model distinguishes well between benign and malicious samples. The _Diagnostic Odds Ratio_ (Equation (4c)) was also used to calculate the ratio of PLR to NLR, in which the higher values are better. The _Youden's Index_ measures incorrect predictions. A model producing no False Positives and no False Negatives would have a Youden's index of 1 ((Equation (4d))). According [17] the _Number Needed to Diagnose (NND)_ (Equation (4e)) metric describes the number of samples needed for the model to make a correct positive prediction. The _Number Needed to Misdiagnose (NNM)_ metric is the number of samples needed for the model to make one incorrect prediction. In this formula, \(n\) is the total number of samples (Equation (4f)). The _Net Benefit (NB)_ determines whether the predictive model has provided a correct or incorrect prediction based on a cutoff point in terms of a probability threshold (P) (Equation (4g)). The idea is taken from the medical field [44]. NB is used to balance the value of an investigation, such as a biopsy, against the potential for harm, such as conducting unnecessary biopsies for a given indication that a condition may exist. Unnecessary biopsies have associated risks such as infection, patient pain, and the use of resources. NB is used in the medical field to apply weight, which [PERSON] et al. refer to as an 'exchange rate', to the disadvantages of investigating false positives compared to the harm of missing a positive diagnosis. It should also be noted that in [PERSON] et al., N refers to the total sum of true and false-positive results--negative results are not in scope. [PERSON] et al. 's original formula was used when calculating NB. _Receiver Operator Curves (ROC)_ are a way of graphically depicting the effectiveness of a binary classification model. They are frequently used to assess the diagnostic value of clinical tests [45]. In a ROC, the True Positive Rate (sensitivity) is plotted on the \(y\)-axis against the False Positive Rate on the \(x\)-axis. The \(x\)-axis is usually titled \"1--specificity\" in clinical contexts, where specificity is the True Negative Rate. The value of (1--specificity) is the same as the False Positive Rate. The \(x\)-axis is usually labelled \"False Positive Rate\" in machine learning contexts. An ideal classifier would have an AUC of 1, and a classifier with no ability to discriminate would have an AUC of 0.5. \[\text{False}\text{\emph{Negative} Rate}\text{\emph{FNR}}=\frac{\text{\emph{FN}}}{ \text{\emph{FN}}+\text{\emph{TP}}} \tag{4a}\] \[\text{\emph{Negative} Likelihood Ratio}\text{\emph{NLR}}=\frac{\text{\emph{TPR}}-1}{ \text{\emph{TNR}}}\] (4b) \[\text{\emph{Diagnostic} Odds Ratio}\text{\emph{DOR}}=\frac{\text{\emph{PLR}}}{ \text{\emph{NLR}}}\] (4c) \[\text{\emph{Youdent}}^{\prime}\text{\emph{s} index}\text{\emph{J}}\text{ \emph{=} }\text{\emph{TPR}}+\text{\emph{TNR}}-1\] (4d) \[\text{\emph{Number Needed to Diagnose}}\text{\emph{NND}}\text{ \emph{=}}\frac{1}{\left[\text{\emph{TPR}}-(1-\text{\emph{TNR}})\right] \right\}\] (4e) \[\text{\emph{NM}}=\frac{1}{\left[1-\frac{(\text{\emph{TP}}+\text{\emph{TN}})} {n}\right]}\] (4f) \[\text{\emph{Net Benefit}}\text{\emph{NB}}=\left(\frac{\text{\emph{TP}}}{n}- \frac{\text{\emph{FP}}}{n}\right)\frac{\text{\emph{P}}}{1-\text{\emph{P}}} \tag{4g}\]  
Equations (4a-g): Novel metrics, (a) Positive Likelihood Ratio, (b) Negative Likelihood Ratio, (c) Diagnostic Odds Ratio, (d) Youden's Index, (e) Number Needed to Diagnose, (f) Number Needed to Misdiagnose, (g) Net Benefit.