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Table 6 Molecular subtype classification accuracy of Breast Cancer for the 100 features

From: Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

  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