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Table 5 Molecular subtype classification accuracy of Breast Cancer for the 1046 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 0.1818 0.9348 0.5396 0.9333 0.5172 0.7987
Random Forest 0.0606 0.9724 0.4532 0.9630 0.0345 0.7657
Logistic Regression 0.1212 0.8747 0.5540 0.9259 0.4483 0.7606
Passive Aggressive 0.1515 0.8622 0.5612 0.9111 0.4483 0.7539
SGD 0.3030 0.9073 0.4604 0.9556 0.4655 0.7752
SVC 0.2727 0.8797 0.5252 0.9185 0.5345 0.7697
Ridge 0.1515 0.7293 0.4317 0.3704 0.2759 0.5524
Bagging 0.3333 0.9298 0.5108 0.9704 0.4310 0.7973
Average 0.1970 0.8863 0.5045 0.8685 0.3944 0.7467
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