h2o::h2o.randomForest() fits a model that creates a large number of decision trees, each independent of the others. The final prediction uses all predictions from the individual trees and combines them.

## Details

For this engine, there are multiple modes: classification and regression

### Tuning Parameters

This model has 3 tuning parameters:

• trees: # Trees (type: integer, default: 50L)

• min_n: Minimal Node Size (type: integer, default: 1)

• mtry: # Randomly Selected Predictors (type: integer, default: see below)

mtry depends on the number of columns and the model mode. The default in h2o::h2o.randomForest() is floor(sqrt(ncol(x))) for classification and floor(ncol(x)/3) for regression.

### Translation from parsnip to the original package (regression)

agua::h2o_train_rf() is a wrapper around h2o::h2o.randomForest().

rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("h2o") %>%
set_mode("regression") %>%
translate()

## Random Forest Model Specification (regression)
##
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), mtries = integer(1),
##     ntrees = integer(1), min_rows = integer(1))

min_rows() and min_cols() will adjust the number of neighbors if the chosen value if it is not consistent with the actual data dimensions.

### Translation from parsnip to the original package (classification)

rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("h2o") %>%
set_mode("classification") %>%
translate()

## Random Forest Model Specification (classification)
##
## Main Arguments:
##   mtry = integer(1)
##   trees = integer(1)
##   min_n = integer(1)
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), mtries = integer(1),
##     ntrees = integer(1), min_rows = integer(1))

### Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables are not required for this model.

### Initializing h2o

To use the h2o engine with tidymodels, please run h2o::h2o.init() first. By default, This connects R to the local h2o server. This needs to be done in every new R session. You can also connect to a remote h2o server with an IP address, for more details see h2o::h2o.init().

You can control the number of threads in the thread pool used by h2o with the nthreads argument. By default, it uses all CPUs on the host. This is different from the usual parallel processing mechanism in tidymodels for tuning, while tidymodels parallelizes over resamples, h2o parallelizes over hyperparameter combinations for a given resample.

h2o will automatically shut down the local h2o instance started by R when R is terminated. To manually stop the h2o server, run h2o::h2o.shutdown().