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Table 3 Parameters and software implementations of the classification algorithms

From: A comprehensive evaluation of multicategory classification methods for microbiomic data

Method Parameter Value Software implementation
SVM, Linear default C (penalty parameter) 1 libsvm [25, 26] (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)
SVM, Linear optimized C (penalty parameter) optimized over (0.01, 0.1, 1, 10, 100)
SVM, Polynomial C (penalty parameter) optimized over (0.01, 0.1, 1, 10, 100)
q (polynomial degree) optimized over (1, 2, 3)
SVM, RBF C (penalty parameter) optimized over (0.01, 0.1, 1, 10, 100)
γ (determines RBF width) optimized over (0.01, 0.1, 1, 10, 100)/number of variables
KRR, Polynomial λ (ridge) optimized over (10-10, 10-9, …, 1) clop [15, 27, 28] (clopinet.com/CLOP/)
q (polynomial degree) optimized over (1, 2, 3)
KRR, RBF λ (ridge) optimized over (10-10, 10-9, …, 1)
γ (determines RBF width) optimized over (0.01, 0.1, 1, 10, 100)/number of variables
KNN, default K = 1 K (number of neighbors) 1 Matlab Statistics Toolbox (http://www.mathworks.com)
KNN, default K = 5 K (number of neighbors) 5
KNN, optimized K (number of neighbors) optimized over (1, …, 50)
PNN σ (spread) optimized over (0.01, 0.02, …, 1) Matlab Neural Network Toolbox (http://www.mathworks.com)
L2-LR, default C (penalty parameter) 1 liblinear [16, 17] (http://www.csie.ntu.edu.tw/~cjlin/liblinear/)
L2-LR, optimized C (penalty parameter) optimized over (0.01, 0.1, 1, 10, 100)
L1-LR, default C (penalty parameter) 1
L1-LR, optimized C (penalty parameter) optimized over (0.01, 0.1, 1, 10, 100)
BLR, Gaussian priors v (variance) automatically determined in the software by cross-validation bbr (http://www.bayesianregression.org)
BLR, Laplace priors v (variance) automatically determined in the software by cross-validation
RF, default ntree (number of trees) 500 R package randomForest (cran.r-project.org/)
mtry (number of variables sampled at each split) number of variables
RF, optimized ntree (number of trees) optimized over (500, 1000, 2000)
mtry (number of variables sampled at each split) optimized over 0.5 , 1 , 2 × number of variables