Table 3.
Performance metrics for binary classification algorithms.
| Classification algorithms | Optimal Parameterization |
Performance metrics Mean value (standard deviation) |
||||
|---|---|---|---|---|---|---|
| ACC | PRE | REC | F1 score | AUC | ||
| BDT | No. of Leaves: 16 Learning rate: 0.05 No. of trees: 100 |
0.724 (0.048) | 0.714 (0.037) | 0.7037 (0.063) | 0.7088 (0.052) | 0.717 (0.053) |
| DF | Random split Count: 128 Maximum Depth: 32 No. of decision trees: 16 |
0.7317 (0.021) | 0.7421 (0.017) | 0.7892 (0.081) | 0.7649 (0.025) | 0.755 (0.017) |
| NN | Learning rate: 0.001 No. of hidden Nodes: 314 |
0.711 (0.031) | 0.7271 (0.043) | 0.7188 (0.018) | 0.7229 (0.029) | 0.7616 (0.095) |
| LoR | Optimization Tolerance: 1e-06 L1 regularization weight: 1 Memory size for L-BFGS: 18 |
0.6741 (0.019) | 0.6805 (0.024) | 0.6161 (0.027) | 0.6467 (0.019) | 0.6874 (0.065) |
| SVM | Lambda – 0.001 | 0.694 (0.017) | 0.673 (0.074) | 0.6027 (0.019) | 0.6359 (0.011) | 0.6619 (0.037) |