## 7 Results
### Results of Machine Learning Models Based on Resampling Approach 2  
As indicated in Section 7.2, the results of the ML models of classification measurements indexes based on the resampling approach 1 are extremely good. In this section, we implement ML models based on the resampling approach 2 to analyze the reliability of the results mentioned in Section 7.2. The rates of the seven machine learning algorithms for CCF detection with SMOTE and ADASYN are illustrated in Tables 10 and 11, respectively. Unlike the results of resampling approach 1, it is shown that the TP samples are less than the TN sample based on the ML algorithms. The fundamental classification performance metrics of seven ML algorithms for CCF detection with SMOTE and ADASYN based on the resampling approach 2 are revealed in Figures 18 and 19, respectively. According to Figure 18, the fundamental measurements indexes of RF with SMOTE are quite good at above 80%, whereas there is a significant difference between precision and other indexes in LR and AdaBoost with ADASYN. The precision of LR and AdaBoost is only about 6.04% and 10.54%. Similarly, as shown in Figure 19, RF with ADASYN also shows high outcomes in all indexes, while LR and AdaBoost with ADASYN show very low results of precision, at approximately 1.81% \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rate**} & **RF with** & **KNN** & **DT with** & **LR with** & **AdaBoost** & **XGBoost** & **DNN** \\  & **SMOTE** & **with** & **SMOTE** & **SMOTE** & **with** & **with** & **with** \\  & & **SMOTE** & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** \\ \hline TP & 120 & 127 & 113 & 135 & 128 & 123 & 126 \\ FP & 14 & 127 & 168 & 2100 & 1086 & 37 & 180 \\ TN & 85,282 & 85,169 & 85,128 & 83,196 & 84,210 & 85,259 & 85,156 \\ FN & 27 & 20 & 34 & 12 & 19 & 24 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: TP, FP, TN, FN rates of machine learning algorithms with SMOTE based on resampling approach 2. Figure 17: AUC measurement performance of machine learning algorithms with ADASYN based on resampling approach 1.  
and 4.57%, respectively. These results are opposite to the ones based on the resampling approach 1. In general, the results of RF and XGBoost based on the resampling approach 2 are similar to their results with the resampling approach 1. Moreover, as shown in Figures 20 and 21, LR and AdaBoost with SMOTE and ADASYN based on the resampling approach 2 show a significant fluctuation among the indexes of combined metrics. The values of the G-mean and balanced accuracy are much higher than the F1 score and MCC values. The values of the F1 score are only about 11.34% and 18.81% based on LR and AdaBoost, respectively. Overall, RF and XGBoost show reliable results in terms of the combined metrics in both resampling approaches. Figure 19: Fundamental classification performance measurements of ML algorithms with ADASYN based on resampling approach 2. Figure 18: Fundamental classification performance measurements of ML algorithms with SMOTE based on resampling approach 2. Based on the resampling approach 2, the positive likelihood ratios are indicated in Table 12. It is obvious that all model contributions are good, according to Table 3. However, there is a dramatic difference between these outcomes of models. The results of RF are very high, at approximately 4973.53, while LR shows very low results, at only 10.69. These results are similar to the ones based on the resampling approach 1. Figure 21: Combined classification performance measurements with ADASYN based on resampling approach 2. Figure 20: Combined classification performance measurements with SMOTE based on resampling approach 2. According to Figures 22 and 23, AUC measurement performance of ML algorithms with SMOTE and ADASYN based on the resampling approach 2 show reliable outcomes, at above 86%. These outcomes are also quite similar to the AUC values based on approach 1.