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  3. Hyperparameter Tuning in R

Exercise

Modeling with h2o

In the last exercise, you successfully prepared data for modeling with h2o. Now, you can use this data to train a model. The h2o library has already been loaded for you, as has the seeds_train_data object 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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  • Train a Random Forest model with h2o.