if (FALSE) {
library(h2o)
h2o.init()
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
cars <- h2o.importFile(f)
predictors <- c("displacement", "power", "weight", "acceleration", "year")
response <- "cylinders"
cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234)
train <- cars_split[[1]]
valid <- cars_split[[2]]
cars_glm <- h2o.glm(balance_classes = TRUE,
seed = 1234,
x = predictors,
y = response,
training_frame = train,
validation_frame = valid)
h2o.coef_norm(cars_glm)
}
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