Table 1.

Accuracy of classifiers used in the experiments on the TCGA dataset

Classifier	Accuracy (10-fold CV)
	1046 Features		100 Features		Hyper parameters	Feature selection method
	avg	std	avg	std
Gradient Boosting	0.9398	0.0076	0.9359	0.0086	300 predictors	Decision Trees
Random Forest	0.9351	0.0071	0.9324	0.0073	300 predictors	Decision Trees
Logistic Regression	0.9178	0.0096	0.9237	0.0067	-	Coefficients
Passive Aggressive	0.9117	0.0104	0.8831	0.0115	-	Coefficients
SGD	0.91	0.0074	0.9035	0.0152	-	Coefficients
SVC	0.9211	0.0122	0.9154	0.0065	Linear kernel	Coefficients
Ridge	0.8971	0.0138	0.8305	0.0062	-	Coefficients
Bagging	0.9151	0.0120	0.9110	0.0077	300 predictors	Decision Trees
Average	0.918463	-	0.9044	-	-	-

In the case a classifier is not using standard values for its hyperparameters, the relevant variations are summarized in the corresponding column