Deep learning in R with h2o
library(MASS) library(h2o) set.seed(123) DataFrame <- Boston #Structure of Boston str(DataFrame) #Histgram of the Boston data hist(DataFrame$medv) #Check the dimention of this data frame dim(DataFrame) head(DataFrame) #min and max value for each of the variable apply(DataFrame,2,range) #scale function will give mean=0 and standard deviation=1 for each variable maxValue <- apply(DataFrame,2,max) minValue <- apply(DataFrame,2,min) DataFrame <- as.data.frame(scale(DataFrame,center = minValue,scale = maxValue-minValue)) #h2o initialization h2o.init(ip="localhost",port = 54321,max_mem_size = "3000m") #Defining x and y y <- "medv" x <- setdiff(colnames(DataFrame),y) #create the train and test data set ind <- sample(1:nrow(DataFrame),400) trainDF <- DataFrame[ind,] testDF <- DataFrame[-ind,] #Fitting the model model <- h2o.deeplearning(x=x, y=y, seed = 1234,
