Table 10 Results of test data for Residual model
Particular | Data | ||
|---|---|---|---|
Model name | Residual | ||
No. of samples | 152 | 152 | 152 |
Total number of features | 354 | 354 | 354 |
ML algorithm used | Random Forest | KSVM | XGBoost |
Feature selection technique | SFFS | SFFS | SFFS |
OverSampling method used | SMOTE | SMOTE | SMOTE |
No. of samples after OverSampling | 270 | 270 | 270 |
Number of synthetic samples | 118 | 118 | 118 |
No. of features used in the dataset | 93 | 312 | 91 |
Mean accuracy train | 0.97 | 1 | 0.99 |
Mean accuracy test | 0.84 | 0.92 | 0.74 |
Sensitivity | 0.85 | 0.72 | 0.50 |
Specificity | 0.89 | 0.97 | 0.74 |
Mean F-Score train label 0 | 0.97 | 1 | 0.99 |
Mean F-Score train label 1 | 0.97 | 1 | 1 |
Mean F-score test label 0 | 0.89 | 0.94 | 0.84 |
Mean F-score test label 1 | 0.68 | 0.87 | 0.25 |
No. of samples for class-0 | 135 | 135 | 135 |
No. of samples for class-1 | 17 | 17 | 17 |
Base algorithm | Random forest | KSVM | XGBoost |
ROC_AUC_Score | 0.94 | 0.99 | 0.91 |