Regression H2O Random Forest Learner

Regression random forest learner. Calls h2o::h2o.randomForest() from package h2o.

H2O Connection

If no running H2O connection is found, the learner will automatically start a local H2O server on 127.0.0.1 via h2o::h2o.init(). If you want to connect to a remote H2O cluster, call h2o::h2o.init() with the appropriate arguments before training or predicting.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.h2o.randomForest")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3extralearners, h2o

Parameters

Id	Type	Default	Levels	Range
build_tree_one_node	logical	FALSE	TRUE, FALSE	-
categorical_encoding	character	AUTO	AUTO, Enum, OneHotInternal, OneHotExplicit, Binary, Eigen, LabelEncoder, SortByResponse, EnumLimited	-
check_constant_response	logical	TRUE	TRUE, FALSE	-
checkpoint	untyped	NULL		-
col_sample_rate_change_per_level	numeric	1		\([0, 2]\)
col_sample_rate_per_tree	numeric	1		\([0, 1]\)
export_checkpoints_dir	untyped	NULL		-
histogram_type	character	AUTO	AUTO, UniformAdaptive, Random, QuantilesGlobal, RoundRobin, UniformRobust	-
ignore_const_cols	logical	TRUE	TRUE, FALSE	-
max_depth	integer	20		\([0, \infty)\)
max_runtime_secs	numeric	0		\([0, \infty)\)
min_rows	numeric	1		\([1, \infty)\)
min_split_improvement	numeric	1e-05		\([0, \infty)\)
mtries	integer	-1		\([1, \infty)\)
nbins	integer	20		\([1, \infty)\)
nbins_cats	integer	1024		\([1, \infty)\)
nbins_top_level	integer	1024		\([1, \infty)\)
ntrees	integer	50		\([1, \infty)\)
sample_rate	numeric	0.632		\([0, 1]\)
score_each_iteration	logical	FALSE	TRUE, FALSE	-
score_tree_interval	integer	0		\([0, \infty)\)
seed	integer	-1		\((-\infty, \infty)\)
stopping_metric	character	AUTO	AUTO, deviance, MSE, RMSE, MAE, RMSLE	-
stopping_rounds	integer	0		\([0, \infty)\)
stopping_tolerance	numeric	0.001		\([0, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-

References

Fryda T, LeDell E, Gill N, Aiello S, Fu A, Candel A, Click C, Kraljevic T, Nykodym T, Aboyoun P, Kurka M, Malohlava M, Poirier S, Wong W (2025). h2o: R Interface for the 'H2O' Scalable Machine Learning Platform. R package version 3.46.0.9, https://github.com/h2oai/h2o-3.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrH2ORandomForest

Methods

Public methods

• LearnerRegrH2ORandomForest$new()

• LearnerRegrH2ORandomForest$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrH2ORandomForest$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrH2ORandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.h2o.randomForest")
print(learner)
#> 
#> ── <LearnerRegrH2ORandomForest> (regr.h2o.randomForest): H2O Random Forest ─────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and h2o
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: missings and weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Model Details:
#> ==============
#> 
#> H2ORegressionModel: drf
#> Model ID:  DRF_model_R_1774260318250_112 
#> Model Summary: 
#>   number_of_trees number_of_internal_trees model_size_in_bytes min_depth
#> 1              50                       50               10702         3
#>   max_depth mean_depth min_leaves max_leaves mean_leaves
#> 1         8    5.78000          6         16    12.42000
#> 
#> 
#> H2ORegressionMetrics: drf
#> ** Reported on training data. **
#> ** Metrics reported on Out-Of-Bag training samples **
#> 
#> MSE:  5.848924
#> RMSE:  2.418455
#> MAE:  2.014721
#> RMSLE:  0.1223739
#> Mean Residual Deviance :  5.848924
#> 
#> 
#> 
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#>  2.36949