R Deep Learning Cookbook

How to do it...

The section will demonstrate steps to build the GLM model using H2O.

1. Now, load the occupancy train and test datasets in R:

# Load the occupancy data occupancy_train <-read.csv("C:/occupation_detection/datatraining.txt",stringsAsFactors = T)occupancy_test <- read.csv("C:/occupation_detection/datatest.txt",stringsAsFactors = T)

2. The following independent (x) and dependent (y) variables will be used to model GLM:

# Define input (x) and output (y) variables"x = c("Temperature", "Humidity", "Light", "CO2", "HumidityRatio")y = "Occupancy"

3. Based on the requirement for H2O, convert the dependent variables into factors as follows:

# Convert the outcome variable into factoroccupancy_train$Occupancy <- as.factor(occupancy_train$Occupancy) ...