LearnerRanger: R6 Class to construct a Ranger learner

In mllrnrs: R6-Based ML Learners for 'mlexperiments'

R6 Class to construct a Ranger learner

Description

The LearnerRanger class is the interface to the ranger R package for use with the mlexperiments package.

Details

Optimization metric:

• classification: classification error rate

• regression: mean squared error Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerRanger

Methods

Public methods

• LearnerRanger$new()

• LearnerRanger$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

---

Method new()

Create a new LearnerRanger object.

Usage

LearnerRanger$new()

Returns

A new LearnerRanger R6 object.

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
  LearnerRanger$new()
}

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

ranger::ranger()

Examples

# binary classification
if (requireNamespace("ranger", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  library(mlbench)
  data("PimaIndiansDiabetes2")
  dataset <- PimaIndiansDiabetes2 |>
    data.table::as.data.table() |>
    na.omit()

  seed <- 123
  feature_cols <- colnames(dataset)[1:8]

  param_list_ranger <- expand.grid(
    num.trees = seq(500, 1000, 500),
    mtry = seq(2, 6, 2),
    min.node.size = seq(1, 9, 4),
    max.depth = seq(1, 9, 4),
    sample.fraction = seq(0.5, 0.8, 0.3)
  )

  train_x <- model.matrix(
    ~ -1 + .,
    dataset[, .SD, .SDcols = feature_cols]
  )
  train_y <- as.integer(dataset[, get("diabetes")]) - 1L

  fold_list <- splitTools::create_folds(
    y = train_y,
    k = 3,
    type = "stratified",
    seed = seed
  )
  ranger_cv <- mlexperiments::MLCrossValidation$new(
    learner = mllrnrs::LearnerRanger$new(),
    fold_list = fold_list,
    ncores = 2,
    seed = 123
  )
  ranger_cv$learner_args <- c(
    as.list(
      data.table::data.table(
        param_list_ranger[37, ],
        stringsAsFactors = FALSE
      ),
    ),
    list(classification = TRUE)
  )
  ranger_cv$performance_metric_args <- list(positive = "1", negative = "0")
  ranger_cv$performance_metric <- mlexperiments::metric("AUC")

  # set data
  ranger_cv$set_data(
    x = train_x,
    y = train_y
  )

  ranger_cv$execute()
}


## ------------------------------------------------
## Method `LearnerRanger$new`
## ------------------------------------------------

if (requireNamespace("ranger", quietly = TRUE)) {
  LearnerRanger$new()
}

mllrnrs documentation built on Jan. 17, 2026, 9:06 a.m.