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]]
This exercise is part of the course
Hyperparameter Tuning in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Train random forest model
rf_model <- ___(___ = x,
___ = y,
___ = train,
___ = valid)