Table 3 Predictive capacity of algorithms fit using raw distribution inherent in sampled data
Random Forest | Support Vector Machine | Extreme Gradient | |||||||
|---|---|---|---|---|---|---|---|---|---|
Other | Defa | Urine | Other | Defa | Urine | Other | Defa | Urine | |
Predicted | |||||||||
Other | 5971 | 39 | 7 | 5972 | 40 | 7 | 5970 | 39 | 7 |
Defa | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 |
Urinea | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Class Statistics | |||||||||
Sensitivity | 1.00 | 0.03 | 0.00 | 1.00 | 0.00 | 0.00 | 1.00 | 0.025 | 0.00 |
Specificity | 0.02 | 1.00 | 1.00 | 0.00 | 1.00 | 1.00 | 0.021 | 1.00 | 1.00 |
PPVa | 0.992 | 0.50 | 0.00 | 0.992 | 0.00 | 0.00 | 0.992 | 0.33 | 0.00 |
NPVa | 0.50 | 0.994 | 0.999 | 0.00 | 0.993 | 0.999 | 0.33 | 0.994 | 0.999 |
Accuracy | 0.51 | 0.51 | 0.50 | 0.50 | 0.50 | 0.50 | 0.51 | 0.51 | 0.50 |
Overall Statistics | |||||||||
Accuracy | 0.992 | 0.992 | 0.992 | ||||||
NIRa | 0.992 | 0.992 | 0.992 | ||||||