## 8 Discussions and Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Definition** & **Formula** & **Reference** \\ \hline \multirow{4}{*}{**Accuracy**} & Measures the ratio of correct classifications & \multirow{2}{*}{\(\frac{\sum_{i=1}^{K}TP_{i}}{N}\)} & \multirow{2}{*}{[108]} \\  & over the total number of samples, and it does & & \\  & not take into account any information on the & & \\  & classification performance of single classes. & & \\ \hline \multirow{4}{*}{**Precision (confidence)**} & The proportion of true positive predictions & \multirow{2}{*}{\(\frac{1}{K}\sum\limits_{i=1}^{K}\frac{TP_{i}}{TP_{i}+FP_{i}}\)} & \multirow{2}{*}{[109]} \\  & among all positive predictions. & & \\  & Indicates the classifier’s ability to avoid wrong & & \\  & predictions in that class. & & \\ \hline \multirow{4}{*}{**Recall (sensitivity)**} & The proportion of actual positives correctly & \multirow{2}{*}{\(\frac{1}{K}\sum\limits_{i=1}^{K}\frac{TP_{i}}{TP_{i}+FN_{i}}\)} & \multirow{2}{*}{[109]} \\  & identified. Indicates the model’s ability to detect positive instances. & & \\ \hline \multirow{4}{*}{**Specificity**} & The proportion of actual negatives correctly & \multirow{2}{*}{\(\frac{1}{K}\sum\limits_{i=1}^{K}\frac{TN_{i}}{TN+FP_{i}}\)} & \multirow{2}{*}{[108]} \\  & identified. Used to measure the ability of a classifier to reject samples of other classes. & & \\ \hline \multirow{4}{*}{**F1-Score**} & The harmonic mean of precision and recall. & \multirow{2}{*}{\(2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\)} & \multirow{2}{*}{[109]} \\  & Balances the two; useful when there is an uneven class distribution. & & \\ \hline \multirow{4}{*}{**F-Score**} & The weighted harmonic mean of precision and recall, & \multirow{2}{*}{\(\frac{1+\beta^{2}}{\beta^{2}\cdot Precision+Recall}\)} & \multirow{2}{*}{[110]} \\  & recall, balancing the two. A generalization of the F1-score. & where \(\beta\) is a parameter that determines the weight of recall & \\ \hline \multirow{4}{*}{**F-statistic**} & A ratio of variances used in ANOVA tests to determine if there are significant differences & \multirow{2}{*}{[111]} \\  & between groups. In regression, it tests the overall significance of the model. & & \\ \hline \multirow{4}{*}{**AUC-ROC**} & Area under the Receiver Operating & \multirow{2}{*}{AUC for discrete classifier = (TPR + TNR)/2} & \multirow{2}{*}{[112]} \\  & Characteristic curve, showing the trade-off & & \\  & between true positive rate and false & & \\  & positive rate. & & \\ \hline \multirow{4}{*}{**Mean Absolute Error (MAE)**} & The average of the absolute differences & \multirow{2}{*}{\(\frac{1}{N}\sum\limits_{i=1}^{N}|y_{i}-\hat{y}_{i}|\)} & \multirow{2}{*}{[113]} \\  & between predicted and actual values. Reflects prediction accuracy for continuous outputs. & & \\ \hline \multirow{4}{*}{**Mean Absolute Percentage Error (MAPE)**} & The average of the absolute percentage errors & \multirow{2}{*}{\(MAPE=\frac{100\%}{N}\sum\limits_{i=1}^{N}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|\)} & \multirow{2}{*}{[113]} \\  & between predicted and actual values. Useful & & \\ \hline \multirow{4}{*}{**Mean Squared Error (MSE)**} & The average of squared differences between predicted and actual values. Penalizes larger errors more heavily. & \multirow{2}{*}{\(MSE=\frac{1}{N}\sum\limits_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}\)} & \multirow{2}{*}{[113]} \\ \cline{1-1} \cline{2-2}  & A version of the MSE measuring magnitude & & \\ \cline{1-1} \cline{2-2}  & errors in the reconstruction of a signal, such as & & \\ \cline{1-1} \cline{2-2}  & for audio or speech tasks. & & \\ \hline \multirow{4}{*}{**Waveform MSE**} & Measures the error in waveform reconstruction & \multirow{4}{*}{[114]} \\  & tasks, commonly used in speech and audio processing. & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Root Mean** \\ **Squared Error** \\ **(RMSE)** \\ \end{tabular} } & The square root of MSE, representing error in the same units as the output variable. & \multirow{2}{*}{\(RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}}\)} & \multirow{2}{*}{[61]} \\ \cline{1-1} \cline{2-2}  & Indicates the proportion of the variance in the & \multirow{2}{*}{\(1-\frac{\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{N} \left(y_{i}-\hat{y}_{i}\right)^{2}}\)} & \multirow{2}{*}{[111]} \\ \cline{1-1} \cline{2-2}  & dependent variable explained by the & & \\ \cline{1-1} \cline{2-2}  & independent variable(s). Used for regression & & \\ \cline{1-1} \cline{2-2}  & models. & & \\ \hline \hline \end{tabular}
\end{table}