Demo2.R

#Setup
rm(list = ls(all = TRUE)) 
gc(reset=TRUE)
set.seed(1234) #From random.org

#Libraries
library(caret)
library(devtools)
install_github('caretEnsemble', 'zachmayer') #Install zach's caretEnsemble package
library(caretEnsemble)

#Data
library(mlbench)
dat <- mlbench.xor(500, 2)
X <- data.frame(dat$x)
Y <- factor(ifelse(dat$classes=='1', 'Yes', 'No'))

#Split train/test
train <- runif(nrow(X)) <= .66

#Setup CV Folds
#returnData=FALSE saves some space
folds=5
repeats=1
myControl <- trainControl(method='cv', number=folds, repeats=repeats, 
                          returnResamp='none', classProbs=TRUE,
                          returnData=FALSE, savePredictions=TRUE, 
                          verboseIter=TRUE, allowParallel=TRUE,
                          summaryFunction=twoClassSummary,
                          index=createMultiFolds(Y[train], k=folds, times=repeats))
PP <- c('center', 'scale')

#Train some models
model1 <- train(X[train,], Y[train], method='gbm', trControl=myControl,
                tuneGrid=expand.grid(.n.trees=500, .interaction.depth=15, .shrinkage = 0.01))
model2 <- train(X[train,], Y[train], method='blackboost', trControl=myControl)
model3 <- train(X[train,], Y[train], method='parRF', trControl=myControl)
model4 <- train(X[train,], Y[train], method='mlpWeightDecay', trControl=myControl, trace=FALSE, preProcess=PP)
model5 <- train(X[train,], Y[train], method='knn', trControl=myControl, preProcess=PP)
model6 <- train(X[train,], Y[train], method='earth', trControl=myControl, preProcess=PP)
model7 <- train(X[train,], Y[train], method='glm', trControl=myControl, preProcess=PP)
model8 <- train(X[train,], Y[train], method='svmRadial', trControl=myControl, preProcess=PP)
model9 <- train(X[train,], Y[train], method='gam', trControl=myControl, preProcess=PP)
model10 <- train(X[train,], Y[train], method='glmnet', trControl=myControl, preProcess=PP)

#Make a list of all the models
all.models <- list(model1, model2, model3, model4, model5, model6, model7, model8, model9, model10)
names(all.models) <- sapply(all.models, function(x) x$method)
sort(sapply(all.models, function(x) min(x$results$ROC)))

#Make a greedy ensemble - currently can only use RMSE
greedy <- caretEnsemble(all.models, iter=1000L)
sort(greedy$weights, decreasing=TRUE)
greedy$error

#Make a linear regression ensemble
linear <- caretStack(all.models, method='glm', trControl=trainControl(method='cv'))
linear$error

#Predict for test set:
library(caTools)
preds <- data.frame(sapply(all.models, function(x){predict(x, X[!train,], type='prob')[,2]}))
preds$ENS_greedy <- predict(greedy, newdata=X[!train,])
preds$ENS_linear <- predict(linear, newdata=X[!train,], type='prob')[,2]
sort(data.frame(colAUC(preds, Y[!train])))

I have two queries:

1. Can we include n.minobsinnode in
   model1 <- train(X, Y, method='gbm', trControl=myControl,tuneGrid=expand.grid(.n.trees=100, .interaction.depth=15, .shrinkage = 0.01))
2. Can we also include weights in model 1 if the training set contains weights also, like we do in normal gbm
   gbmModel = gbm(class~.-weight, data=train, weights=train$weight,verbose=TRUE)
   Thanks
   cosmos

Hi,
gbm needs to have the parameter .n.minobsinnode=10 or other values than 10

model1 <- train(X[train,], Y[train], method='gbm', trControl=myControl,tuneGrid=expand.grid(.n.trees=500, .n.minobsinnode=10, .interaction.depth=15, .shrinkage = 0.01 ))

otherwise the following error occurs.

Error in train.default(X[train, ], Y[train], method = "gbm", trControl = myControl,  : 
  The tuning parameter grid should have columns n.trees, interaction.depth, shrinkage, n.minobsinnode

Also if no parallel backend is registered it will just run on one CPU or throw errors
Also bunch of dependencies are missing if installed in freshly installed R 3.2.2.
Cheers
Tobias