############################# RF_Pred is a function that models response classes using random forrest.
############################# Input variables of this function are as follows:
############################# 1) TrainFeat: Feature frame (rows as samples and columns as features) for training set
############################# 2) TrainObs: Observed classess for training
############################# 3) TestFeat: Feature frame (rows as samples and columns as features) for testing
RF_Pred <- function(TrainFeat, TrainObs, TestFeat){
# Training <- data.frame(TrainFeat)
TrainFeat$Observ <- TrainObs
#############
# nTree <- 15
# nTree <- RF_Optimize(TrainFeat,TrainObs)
# print(paste("Best nTree is ", nTree, sep = "", collapse = ""))
#############
#nTree_grid <- seq(3, 20, 2)
mtry <- sqrt(ncol(TrainFeat))
trnCtrl <- trainControl(method = "repeatedCV",number = 5,repeats = 5)
srchGrid <- expand.grid(.mtry=mtry) #.ntree = nTree_grid,
RF_Model <- caret::train(Observ~., method="rf", data=TrainFeat, #ntree=nTree,
tuneGrid = srchGrid,trControl = trnCtrl)
Testing <- data.frame(TestFeat)
PredVal <- predict(RF_Model, Testing)
return(PredVal)
}
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