LearnerGlmnet: R6 Class to construct a Glmnet learner

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

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

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerGlmnet

Methods

Public methods

• LearnerGlmnet$new()

• LearnerGlmnet$clone()

Inherited methods

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

---

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)
}

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