| PolyBayes |
---|
Probability (P) | TP | FP | PPV |
---|
P ≤ 0.60
| 20 | 1756 | 1.1 |
0.60 < P ≤ 0.70
| 38 | 1529 | 2.4 |
0.70 < P ≤ 0.80
| 31 | 1683 | 1.8 |
0.80 < P ≤ 0.90
| 45 | 2015 | 2.2 |
0.90 < P ≤ 0.95
| 50 | 1613 | 3.0 |
0.95 < P ≤ 0.97
| 53 | 1055 | 4.8 |
0.97 < P ≤ 0.99
| 148 | 2069 | 6.7 |
P = 1.00
| 1050 | 5235 | 16.7 |
Overall
| 1435 | 16955 |
7.8
|
|
ML Predictor
|
|
TP
|
FP
|
PPV
|
Overall
| 1153 | 207 |
84.8
|
- TP: True Positive, FP: False Positive,
- Positive predictive value (PPV) = TP/(TP + FP).
- The number of true positives in the dataset can be increased by using stringent PolyBayes posterior probability cut-off values. However, even when the posterior probability value is set to the maximum of 1.0 the positive predictive value with PolyBayes is less than 20%. Application of machine learning showed a 5–10 fold increase in the PPV at different PolyBayes posterior probability values.