Classification GLM with Elastic Net Regularization Auto Learner

Classification auto learner.

Value

Object of class R6::R6Class and LearnerClassifAutoGlmnet.

Super classes

mlr3::Learner -> LearnerAuto -> LearnerClassifAuto -> LearnerClassifAutoGlmnet

Methods

Public methods

• LearnerClassifAutoGlmnet$new()

• LearnerClassifAutoGlmnet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• 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()
• LearnerAuto$encapsulate()

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LearnerClassifAutoGlmnet$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifAutoGlmnet$new(id = "classif.auto_glmnet", rush = NULL)

Arguments

id

(character(1))
Identifier for the new instance.

rush

rush::Rush
Rush instance.

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LearnerClassifAutoGlmnet$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAutoGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = lrn("classif.auto_glmnet")
learner
#> 
#> ── <LearnerClassifAutoGlmnet> (classif.auto_glmnet) ────────────────────────────
#> • Model: -
#> • Parameters: check_learners=TRUE, devices=cpu, encapsulate_learner=TRUE,
#> encapsulate_mbo=TRUE, initial_design_default=FALSE,
#> initial_design_fraction=0.25, initial_design_set=0, initial_design_size=256,
#> initial_design_type=sobol, large_data_size=1000000, learner_timeout=900,
#> resampling=<ResamplingHoldout>, small_data_resampling=<ResamplingCV>,
#> small_data_size=5000, store_benchmark_result=FALSE, store_models=FALSE,
#> terminator=<TerminatorRunTime>
#> • Packages: mlr3, mlr3tuning, mlr3pipelines, mlr3learners, and glmnet
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, character, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings, multiclass, twoclass, and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'