## 3 Results and Reports
### Classification Performances
#### 3.4.2 Optimizing Results Using the Cross Validation Technique  
Evaluating hyper-parameter (C and Gamma) requires special attention to detect potential 'overfitting' and 'underfitting' problems while gradually eliminating candidate learning models and finally ending up with a single optimal one. Otherwise, the obtained evaluation metrics will not reflect the performance of the generalization. Dividing each dataset into two sets (training/testing) or three sets (training/testing/validation) reduces significantly the number of samples that can be used to learn the model, and this will involve obtaining results depending on the sampling used during the training phase. To avoid this random data-related dependency, applying the cross-validation technique was proven to be the solution. In order to reduce to a minimum, the low efficiency associated with randomly splitting training and testing data set, scientists are likely to use the K-fold approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Classifier** & **Learning** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{Dts\_1} & SVM (RBF) & Train & 0.979 \\  & & Test & 0.951 \\  & LDA & Train & 0.838 \\  & & Test & 0.818 \\  & KNN & Train & 0.978 \\  & & Test & 0.975 \\  & XGBoost & Train & 1 \\  & RF & Test & 1 \\ \hline \multirow{8}{*}{Dts\_2} & SVM (RBF) & Train & 0.991 \\  & & Test & 0.969 \\  & LDA & Train & 0.898 \\  & KNN & Test & 0.963 \\  & XGBoost & Train & 1 \\  & RF & Test & 1 \\ \hline \multirow{8}{*}{Dts\_3} & SVM (RBF) & Train & 0.979 \\  & & Test & 0.969 \\  & LDA & Train & 0.794 \\  & & Test & 0.812 \\  & & Train & 0.979 \\  & & Test & 0.963 \\  & & Test & 0.963 \\  & & Train & 1 \\  & & Test & 1 \\ \hline \multirow{8}{*}{Dts\_1} & SVM (RBF) & Train & 0.979 \\  & & Test & 0.969 \\  & LDA & Train & 0.794 \\  & & Test & 0.812 \\  & & Train & 0.979 \\  & & Test & 0.963 \\  & & Train & 1 \\  & & Test & 1 \\  & & Train & 1 \\  & & Test & 1 \\  & RF & Test & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training and testing results for the generated classifier. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Datasets**} & \multicolumn{2}{c}{**SVM (RBF)**} & \multicolumn{2}{c}{**LDA**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**RF**} \\ \hline \multirow{3}{*}{Dts\_1} & & Class 0 & Class 1 & Class 0 & Class 1 & Class 0 & Class 0 & Class 1 & Class 1 & Class 1 & Class 1 \\ \cline{2-11}  & Class 0 & 114 & 0 & 106 & 8 & 114 & 114 & 114 & 0 & 114 & 0 \\  & Class 1 & 4 & 47 & 22 & 29 & 8 & 8 & 0 & 51 & 0 & 51 \\ \hline \multirow{3}{*}{Dts\_1} & & Class 0 & Class 1 & Class 0 & Class 1 & Class 0 & Class 0 & Class 1 & Class 1 & Class 1 & Class 1 \\ \cline{2-11}  & Class 1 & 125 & 1 & 126 & 0 & 125 & 125 & 126 & 0 & 126 & 0 \\ \cline{2-11}  & Class 1 & 4 & 35 & 17 & 22 & 5 & 5 & 0 & 39 & 0 & 39 \\ \hline \multirow{3}{*}{Dts\_1} & & Class 0 & Class 1 & Class 0 & Class 1 & Class 0 & Class 0 & Class 1 & Class 1 & Class 1 & Class 1 \\ \cline{2-11}  & Class 0 & 91 & 3 & 81 & 13 & 93 & 93 & 94 & 0 & 94 & 0 \\ \cline{1-1}  & Class 1 & 2 & 69 & 18 & 53 & 5 & 5 & 0 & 71 & 0 & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrices of generated results using all classifiers. In this technique, each of the datasets was randomly divided into k subsets of the same scale. Then we tested the shaped classification models k times. The final representative accuracy was calculated as the average of the k precision measurements. Finally, the overall accuracy was calculated as the average of the k accuracies. As detailed in Section 3.3, it was essential to apply a cross validation on our model in order to overcome the problems associated with learning to finally obtain a reliable and generalized model for the entire sample. We had chosen to divide each dataset into five (k = 5) components, and then calculated the accuracy of each part to finally return their average score. Table 9 summarizes the obtained calculations as well as their average.