DL2.H2o.R

#load the package
require(h2o)

#start h2o
localH2o <- h2o.init(nthreads = -1, max_mem_size = "20G")

#load data on H2o
trainh2o <- as.h2o(train)
testh2o <- as.h2o(test)

#set variables
y <- "target"
x <- setdiff(colnames(trainh2o),y)

#train the model - without hidden layer
deepmodel <- h2o.deeplearning(x = x
                            ,y = y
                            ,training_frame = trainh2o
                            ,standardize = T
                            ,model_id = "deep_model"
                            ,activation = "Rectifier"
                            ,epochs = 100
                            ,seed = 1
                            ,nfolds = 5
                            ,variable_importances = T)

#compute variable importance and performance
h2o.varimp_plot(deepmodel,num_of_features = 20)
h2o.performance(deepmodel,xval = T) #84.5 % CV accuracy