Models | Parameters | Parameter ranges | Optimal parameters |
---|---|---|---|
ANN | hidden; linear.output | hidden:c(1,1,1) ~ c(10,10,10); linear.output:FALSE; learningrate: 0.001 ~ 0.050 | hidden = c(10,7,4); linear.output = FALSE learningrate = 0.013 |
DT | method; metric; trControl tuneGrid | method: “C5.0”; metric: “ROC”; trControl: trainControl(method = “cv”,selectionFunction = “oneSE”) tuneGrid* = expand.grid(.model = “tree”,.trials = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)) | method = “C5.0”; metric = “ROC”; trControl = trainControl(method = “cv”,selectionFunction = “oneSE”) tuneGrid* = expand.grid(.model = “tree”,.trials = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)) |
KNN | kernel | Kernel: “triweight”; k: 1 ~ 30; d: 1 ~ 10 | kernel = “triweight”; k = 15; d = 2 |
LR | maxit | maxit: 1 ~ 100 | maxit = 50 |
NB | - | - | - |
RF | method; selectionFunction; metric; trControl | method: “rf”; selectionFunction: “oneSE”; metric: “Kappa”; trControl:trainControl(method = “cv”,selectionFunction = “oneSE”) | method = “rf”; selectionFunction = “oneSE”; metric = “Kappa”; trControl = trainControl(method = “cv”,selectionFunction = “oneSE”) |
SVM | method; tuneLength; trControl | method: “svmRadial”; tuneLength: 1 ~ 50; trControl:trainControl(method = “cv”, selectionFunction = “oneSE”) | method = “svmRadial”; tuneLength = 12; trControl = trainControl(method = “cv”,selectionFunction = “oneSE”,classProbs = TRUE) |