NADIA (version 0.4.2)

PipeOpmissRanger: PipeOpmissRanger

Description

Implements missRanger methods as mlr3 pipeline, more about missRanger autotune_missRanger.

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_missRanger".

• mtry :: integer(1)
  Sample fraction used by missRanger. This param isn't optimized automatically. If NULL default value from ranger package will be used, NULL.

• num.trees :: integer(1)
  Number of trees. If optimize == TRUE. Param set seq(10,num.trees,iter) will be used, default 500

• pmm.k :: integer(1)
  Number of candidate non-missing values to sample from in the predictive mean matching step. 0 to avoid this step. If optimize=TRUE params set: sample(1:pmm.k, iter) will be used. If pmm.k=0, missRanger is the same as missForest, default 5.

• random.seed :: integer(1)
  Random seed, default 123.

• iter :: integer(1)
  Number of iterations for a random search, default 10.

• optimize :: logical(1)
  If set TRUE, function will optimize parameters of imputation automatically. If parameters will be tuned by other method, should be set to FALSE, 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 -> missRanger_imputation

Methods

Public methods

• PipeOpmissRanger$new()

• PipeOpmissRanger$clone()

Inherited methods

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

---

Method new()

Usage

PipeOpmissRanger$new(
  id = "impute_missRanger_B",
  maxiter = 10,
  random.seed = 123,
  mtry = NULL,
  num.trees = 500,
  pmm.k = 5,
  optimize = FALSE,
  iter = 10,
  out_file = NULL
)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpmissRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{

 # Using debug learner for example purpose

  graph <- PipeOpmissRanger$new() %>>% LearnerClassifDebug$new()
  graph_learner <- GraphLearner$new(graph)

  # Task with NA

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