Table 6.
Molecular subtype classification accuracy of Breast Cancer for the 100 features
| Normal | LumA | LumB | TNBC | Her2 | Global | |
|---|---|---|---|---|---|---|
| #Samples | 33 | 399 | 139 | 135 | 58 | 764 |
| Gradient Boosting | 0.2424 | 0.9248 | 0.5324 | 0.9333 | 0.5517 | 0.7975 |
| Random Forest | 0.2121 | 0.9599 | 0.4029 | 0.9704 | 0.2069 | 0.7712 |
| Logistic Regression | 0.2727 | 0.8997 | 0.4892 | 0.9037 | 0.5517 | 0.7727 |
| Passive Aggressive | 0.3939 | 0.8546 | 0.4460 | 0.8667 | 0.5000 | 0.7358 |
| SGD | 0.4545 | 0.8897 | 0.4460 | 0.8444 | 0.4310 | 0.7475 |
| SVC | 0.5152 | 0.8446 | 0.5108 | 0.9037 | 0.5517 | 0.7581 |
| Ridge | 0.0606 | 0.9474 | 0.4388 | 0.8593 | 0.3966 | 0.7594 |
| Bagging | 0.2727 | 0.9173 | 0.4964 | 0.9481 | 0.3793 | 0.7777 |
| Average | 0.3030 | 0.9048 | 0.4703 | 0.9037 | 0.4461 | 0.7650 |