The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Title: R6-Based ML Learners for 'mlexperiments'
Version: 0.0.8
Description: Enhances 'mlexperiments' https://CRAN.R-project.org/package=mlexperiments with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' https://CRAN.R-project.org/package=glmnet, 'ranger' https://CRAN.R-project.org/package=ranger, 'xgboost' https://CRAN.R-project.org/package=xgboost, and 'lightgbm' https://CRAN.R-project.org/package=lightgbm. These can be used directly with the 'mlexperiments' R package.
License: GPL (≥ 3)
URL: https://github.com/kapsner/mllrnrs
BugReports: https://github.com/kapsner/mllrnrs/issues
Depends: R (≥ 4.1.0)
Imports: data.table, kdry, mlexperiments (≥ 1.0.0), R6, stats
Suggests: glmnet, lightgbm (≥ 4.0.0), lintr, measures, mlbench, parallel, quarto, ranger, rBayesianOptimization, splitTools, testthat (≥ 3.0.1), xgboost (≥ 3.1.2.1)
VignetteBuilder: quarto
Config/testthat/edition: 3
Config/testthat/parallel: false
Date/Publication: 2026-01-17 06:10:16 UTC
Encoding: UTF-8
SystemRequirements: Quarto command line tools (https://github.com/quarto-dev/quarto-cli).
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-01-16 20:26:49 UTC; user
Author: Lorenz A. Kapsner ORCID iD [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>
Repository: CRAN

R6 Class to construct a Glmnet learner

Description

The LearnerGlmnet class is the interface to the glmnet R package for use with the mlexperiments package.

Details

Optimization metric: Can be used with

Super class

mlexperiments::MLLearnerBase -> LearnerGlmnet

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerGlmnet object.

Usage
LearnerGlmnet$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

Returns

A new LearnerGlmnet R6 object.

Examples
if (requireNamespace("glmnet", quietly = TRUE)) {
  LearnerGlmnet$new(metric_optimization_higher_better = FALSE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerGlmnet$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

glmnet::glmnet(), glmnet::cv.glmnet()

Examples

# binary classification
if (requireNamespace("glmnet", 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]

  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
  )
  glmnet_cv <- mlexperiments::MLCrossValidation$new(
    learner = mllrnrs::LearnerGlmnet$new(
      metric_optimization_higher_better = FALSE
    ),
    fold_list = fold_list,
    ncores = 2,
    seed = 123
  )
  glmnet_cv$learner_args <- list(
    alpha = 1,
    lambda = 0.1,
    family = "binomial",
    type.measure = "class",
    standardize = TRUE
  )
  glmnet_cv$predict_args <- list(type = "response")
  glmnet_cv$performance_metric_args <- list(positive = "1", negative = "0")
  glmnet_cv$performance_metric <- mlexperiments::metric("AUC")

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

  glmnet_cv$execute()
}

## ------------------------------------------------
## Method `LearnerGlmnet$new`
## ------------------------------------------------

if (requireNamespace("glmnet", quietly = TRUE)) {
  LearnerGlmnet$new(metric_optimization_higher_better = FALSE)
}

R6 Class to construct a LightGBM learner

Description

The LearnerLightgbm class is the interface to the lightgbm R package for use with the mlexperiments package.

Details

Optimization metric: needs to be specified with the learner parameter metric. The following options can be set via options():

LearnerLightgbm can be used with

Super class

mlexperiments::MLLearnerBase -> LearnerLightgbm

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerLightgbm object.

Usage
LearnerLightgbm$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

Returns

A new LearnerLightgbm R6 object.

Examples
if (requireNamespace("lightgbm", quietly = TRUE)) {
  LearnerLightgbm$new(metric_optimization_higher_better = FALSE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerLightgbm$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

lightgbm::lgb.train(), lightgbm::lgb.cv()

Examples

# binary classification
if (requireNamespace("lightgbm", 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_lightgbm <- expand.grid(
    bagging_fraction = seq(0.6, 1, .2),
    feature_fraction = seq(0.6, 1, .2),
    min_data_in_leaf = seq(10, 50, 10),
    learning_rate = seq(0.1, 0.2, 0.1),
    num_leaves = seq(10, 50, 10),
    max_depth = -1L
  )

  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
  )
  lightgbm_cv <- mlexperiments::MLCrossValidation$new(
    learner = mllrnrs::LearnerLightgbm$new(
      metric_optimization_higher_better = FALSE
    ),
    fold_list = fold_list,
    ncores = 2,
    seed = 123
  )
  lightgbm_cv$learner_args <- c(
    as.list(
      data.table::data.table(
        param_list_lightgbm[37, ],
        stringsAsFactors = FALSE
      ),
    ),
    list(
      objective = "binary",
      metric = "binary_logloss"
    ),
    nrounds = 45L
  )
  lightgbm_cv$performance_metric_args <- list(positive = "1", negative = "0")
  lightgbm_cv$performance_metric <- mlexperiments::metric("AUC")

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

  lightgbm_cv$execute()
}


## ------------------------------------------------
## Method `LearnerLightgbm$new`
## ------------------------------------------------

if (requireNamespace("lightgbm", quietly = TRUE)) {
  LearnerLightgbm$new(metric_optimization_higher_better = FALSE)
}

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:

Super class

mlexperiments::MLLearnerBase -> LearnerRanger

Methods

Public methods

Inherited methods

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()
}

R6 Class to construct a Xgboost learner

Description

The LearnerXgboost class is the interface to the xgboost R package for use with the mlexperiments package.

Details

Optimization metric: needs to be specified with the learner parameter eval_metric. The following options can be set via options():

LearnerXgboost can be used with

Super class

mlexperiments::MLLearnerBase -> LearnerXgboost

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerXgboost object.

Usage
LearnerXgboost$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

Returns

A new LearnerXgboost R6 object.

Examples
if (requireNamespace("xgboost", quietly = TRUE)) {
  LearnerXgboost$new(metric_optimization_higher_better = FALSE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerXgboost$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

xgboost::xgb.train(), xgboost::xgb.cv()

Examples

if (requireNamespace("xgboost", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  # binary classification
  Sys.setenv("OMP_THREAD_LIMIT" = 2)

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

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

  param_list_xgboost <- expand.grid(
     subsample = seq(0.6, 1, .2),
     colsample_bytree = seq(0.6, 1, .2),
     min_child_weight = seq(1, 5, 4),
     learning_rate = seq(0.1, 0.2, 0.1),
     max_depth = seq(1, 5, 4),
     nthread = 2
  )

  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
  )
  xgboost_cv <- mlexperiments::MLCrossValidation$new(
    learner = mllrnrs::LearnerXgboost$new(
      metric_optimization_higher_better = FALSE
    ),
    fold_list = fold_list,
    ncores = 2L,
    seed = 123
  )
  xgboost_cv$learner_args <- c(
    as.list(
      data.table::data.table(
        param_list_xgboost[37, ],
        stringsAsFactors = FALSE
      ),
    ),
    list(
      objective = "binary:logistic",
      eval_metric = "logloss"
    ),
    nrounds = 45L
  )
  xgboost_cv$performance_metric_args <- list(positive = "1", negative = "0")
  xgboost_cv$performance_metric <- mlexperiments::metric("AUC")

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

  xgboost_cv$execute()
}


## ------------------------------------------------
## Method `LearnerXgboost$new`
## ------------------------------------------------

if (requireNamespace("xgboost", quietly = TRUE)) {
  LearnerXgboost$new(metric_optimization_higher_better = FALSE)
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.