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Table 9 Comparison of the 8 classifiers, for the different experiments with the 100-miRNA signature

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

   TT vs   TCGA GEO  
Classifier TCGA NT GEO (Subtype) (Subtype) Global
Gradient Boosting 0.9359 0.9846 0.6697 0.9725 0.8909 0.8907
Random Forest 0.9324 0.9839 0.8085 0.9725 0.8634 0.9121
Logistic Regression 0.9237 0.9799 0.9351 0.9647 0.8476 0.9302
Passive Aggressive 0.8831 0.9606 0.8678 0.9556 0.8197 0.8974
SGD 0.9035 0.9767 0.9393 0.9490 0.8145 0.9166
SVC 0.9154 0.9791 0.7724 0.9451 0.8355 0.8895
Ridge 0.8305 0.9470 0.8867 0.9503 0.8300 0.8889
Bagging 0.9110 0.9812 0.7682 0.9555 0.9070 0.9046
  1. Logistic Regression was the best across all experiments, and Ridge has the worst accuracy
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