PipeOpVIM_regrImp {NADIA}

PipeOpVIM_regrImp

Description

Implements Regression Imputation methods as mlr3 pipeline, more about RI autotune_VIM_regrImp.

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

Parameters

The parameters include inherited from ['PipeOpImpute'], as well as:

• id :: character(1)
  Identifier of resulting object, default "imput_VIM_regrImp".

• robust :: logical(1)
  TRUE/FALSE: whether to use robust regression, default FALSE.

• mod_cat :: logical(1)
  TRUE/FALSE if TRUE for categorical variables the level with the highest prediction probability is selected, otherwise it is sampled according to the probabilities, default FALSE.

• use_imputed :: logical(1)
  TRUE/FALSe: if TURE, already imputed columns will be used to impute others, default FALSE.

• out_fill :: character(1)
  Output log file location. If file already exists log message will be added. If NULL no log will be produced, default NULL.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpImpute -> VIM_regrImp_imputation

Methods

Public methods

• PipeOpVIM_regrImp$new()

• PipeOpVIM_regrImp$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

Method new()

Usage

PipeOpVIM_regrImp$new(
  id = "impute_VIM_regrImp_B",
  robust = FALSE,
  mod_cat = FALSE,
  use_imputed = FALSE,
  out_file = NULL
)

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpVIM_regrImp$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

{
  graph <- PipeOpVIM_regrImp$new() %>>% mlr3learners::LearnerClassifGlmnet$new()
  graph_learner <- GraphLearner$new(graph)

  # Task with NA

  resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))
}