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  3. Hyperparameter Tuning in R

Exercise

AutoML in h2o

A very convenient functionality of h2o is automatic machine learning (AutoML). The h2o library and seeds_train_data have already been loaded for you and the following code has been run:

h2o.init()
seeds_train_data_hf <- as.h2o(seeds_train_data)

y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)

seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])

sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]

Instructions 1/4

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  • Define an automatic machine learning training with maximum runtime of 10 seconds. Note: 10 seconds is of course not enough for "real-life" but training for hours would take too long for the purpose of this exercise otherwise.