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(), weights = missing_arg(),
## validation_frame = 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(), weights = missing_arg(),
## validation_frame = 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().