Table 1.
The accuracy score of correct predictions are obtained for unbalanced and balanced data, for the various algorithms employed in this paper. The most accurate results are obtained for balanced data by use of the Support Vector Machine with rbf kernel (regSVM-rbf).
| Balanced Data | |||
|---|---|---|---|
| ML Algorithms | Unbalanced Data | Weight | SMOTE |
| regRF | 0.747 | 0.826 | 0.833 |
| regLOG-l1 | 0.700 | 0.776 | 0.783 |
| regLOG-l2 | 0.720 | 0.798 | 0.783 |
| regSVM-lin | 0.867 | 0.725 | 0.808 |
| regSVM-rbf | 0.662 | 0.917 | 0.933 |
| regSVM-sigmoid | 0.555 | 0.543 | 0.4042 |
| ERT | 0.722 | 0.776 | 0.750 |