Regression Gradient Boosted Decision Trees Auto Learner

Regression auto learner.

Value

Object of class R6::R6Class and LearnerRegrAutoCatboost.

Super classes

mlr3::Learner -> LearnerAuto -> LearnerRegrAuto -> LearnerRegrAutoCatboost

Methods

Public methods

• LearnerRegrAutoCatboost$new()

• LearnerRegrAutoCatboost$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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LearnerRegrAutoCatboost$new()

Creates a new instance of this R6 class.

Usage

LearnerRegrAutoCatboost$new(id = "regr.auto_catboost", rush = NULL)

Arguments

id

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

rush

rush::Rush
Rush instance.

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LearnerRegrAutoCatboost$clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrAutoCatboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = lrn("regr.auto_catboost")
learner
#> 
#> ── <LearnerRegrAutoCatboost> (regr.auto_catboost) ──────────────────────────────
#> • 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, mlr3extralearners,
#> and catboost
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, character, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: missings and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'