Table 2 The mean cross-validation scores, standard deviation (SD), and the accuracy from different algorithms for the SMOTE and original datasets. The table illustrates the mean K-fold cross-validation scores the corresponding SDs acquired by each classification algorithm with and without the application of the SMOTE.
Algorithm | Mean accuracy | Standard deviation (SD) | Accuracy as percentage | |
|---|---|---|---|---|
With SMOTE | KNN | 0.6121 | 0.0340 | 61.21% |
LR | 0.6440 | 0.0360 | 64.44% | |
RF | 0.8723 | 0.0209 | 87.23% | |
DTC | 0.8175 | 0.0197 | 81.75% | |
GaussianNB | 0.6914 | 0.0315 | 69.14% | |
SVM | 0.6833 | 0.0414 | 68.33% | |
AdaBoost Classifier | 0.7356 | 0.0225 | 73.56% | |
Without SMOTE | KNN | 0.7418 | 0.0185 | 74.18% |
LR | 0.7761 | 0.0034 | 77.61% | |
RF | 0.8989 | 0.0224 | 89.89% | |
DTC | 0.8653 | 0.0193 | 86.53% | |
GaussianNB | 0.7508 | 0.0193 | 75.08% | |
SVM | 0.7720 | 0.0029 | 77.20% | |
AdaBoost Classifier | 0.8183 | 0.0210 | 81.80% |