Classification FT-Transformer Auto Learner

Classification auto learner.

Value

Object of class R6::R6Class and LearnerClassifAutoFTTransformer.

Super classes

mlr3::Learner -> LearnerAuto -> LearnerClassifAuto -> LearnerClassifAutoFTTransformer

Methods

Public methods

• LearnerClassifAutoFTTransformer$new()

• LearnerClassifAutoFTTransformer$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()

---

LearnerClassifAutoFTTransformer$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifAutoFTTransformer$new(
  id = "classif.auto_ft_transformer",
  rush = NULL
)

Arguments

id

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

rush

rush::Rush
Rush instance.

---

LearnerClassifAutoFTTransformer$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAutoFTTransformer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = lrn("classif.auto_ft_transformer")
learner
#> 
#> ── <LearnerClassifAutoFTTransformer> (classif.auto_ft_transformer) ─────────────
#> • 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,
#> memory_limit=32000, n_threads=1, 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 mlr3torch
#> • 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'