if (FALSE) {
library(h2o)
h2o.init()
# Import the wine dataset into H2O:
f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv"
df <- h2o.importFile(f)
# Set the response
response <- "quality"
# Split the dataset into a train and test set:
splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1)
train <- splits[[1]]
test <- splits[[2]]
# Build and train the model:
aml <- h2o.automl(y = response,
training_frame = train,
max_models = 10,
seed = 1)
# Create the variable importance heatmap
varimp_heatmap <- h2o.varimp_heatmap(aml)
print(varimp_heatmap)
}
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