Table 15 Comparison of the performance of Random Forest (RF) classifier having 10, 50 and 100 trees with different feature set considered at each split (1, 2, 4, 8, and 12) without using sampling
| Â | No. of tree =10 | No. of tree =50 | No. of tree =100 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F = 1 | F = 2 | F = 4 | F = 8 | F = 12 | F = 1 | F = 2 | F = 4 | F = 8 | F = 12 | F = 1 | F = 2 | F = 4 | F = 8 | F = 12 | |
Sensitivity | 45.82% | 47.20% | 48.35% | 46.64% | 45.44% | 56.62% | 56.34% | 57.33% | 55.84% | 54.51% | 58.39% | 59.87% | 59.09% | 56.56% | 54.41% |
Specificity | 90.03% | 90.23% | 90.58% | 90.83% | 90.85% | 89.60% | 89.81% | 90.29% | 90.48% | 90.57% | 89.50% | 89.81% | 90.21% | 90.45% | 90.48% |
Precision | 18.90% | 20.77% | 24.19% | 26.91% | 27.30% | 13.61% | 15.74% | 20.41% | 22.42% | 23.42% | 12.49% | 15.53% | 19.52% | 22.08% | 22.56% |
F-score | 26.76% | 28.84% | 32.24% | 34.13% | 34.11% | 21.95% | 24.61% | 30.10% | 31.99% | 32.76% | 20.58% | 24.66% | 29.35% | 31.76% | 31.90% |
RMSE | 0.3 | 0.3 | 0.3 | 0.3 | 0.31 | 0.29 | 0.29 | 0.29 | 0.29 | 0.30 | 0.29 | 0.29 | 0.29 | 0.29 | 0.29 |
AUC | 0.76 | 0.77 | 0.77 | 0.77 | 0.76 | 0.81 | 0.81 | 0.81 | 0.81 | 0.80 | 0.81 | 0.81 | 0.82 | 0.81 | 0.81 |