set.seed(18221)
# This example is taken from the gbm package documentation.
# create some data
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
X5 <- factor(sample(letters[1:3],N,replace=TRUE))
X6 <- 3*runif(N)
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)
Y1<-Y
Y<-ifelse((Y1-min(Y1))/(max(Y1)-min(Y1))<0.5,0,1)
# introduce some missing values
X1[sample(1:N,size=500)] <- NA
X4[sample(1:N,size=300)] <- NA
thedata <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)

library(gbm)
library(gbm2sas)
library(h2o)
localH2O &lt;- h2o.init(max_mem_size = &#039;1g&#039;)

#gbm1 <-
#gbm(Y~X1+X2+X3+X4+X5+X6,
#data=thedata,
#var.monotone=c(0,0,0,0,0,0),

#distribution="adaboost",
#n.trees=100,
#shrinkage=0.05,
#interaction.depth=3,
#bag.fraction = 0.5,
#train.fraction = 1,
#n.minobsinnode = 10,
#cv.folds = 3,
#keep.data=TRUE,
#verbose=FALSE,
#n.cores=1)
thedata2<-as.h2o(thedata)
iris.gbm.h2o <- h2o.gbm(Y~X1+X2+X3+X4+X5+X6, data = thedata2, distribution = "bernoulli", 
n.trees = 10, interaction.depth = 3, n.minobsinnode = 10, shrinkage = 0.01) 

print(iris.gbm.h2o)