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NADIA (version 0.4.2)

PipeOpmissForest: PipeOpmissForest

Description

Implements missForest methods as mlr3 pipeline more about missForest autotune_missForest

Arguments

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_missForest".

  • cores :: integer(1)
    Number of threads used by parallel calculations. If NULL approximately half of available CPU cores will be used, default NULL.

  • ntree_set :: integer(1)
    Vector with number of trees values for grid search, used only when optimize=TRUE, default c(100,200,500,1000).

  • mtry_set :: integer(1)
    Vector with number of variables values randomly sampled at each split, used only when optimize=TRUE, default NULL.

  • parallel :: logical(1)
    If TRUE parallel calculations are used, default FALSE.

  • ntree :: integer(1)
    ntree from missForest function, default 100.

  • 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.

  • mtry :: integer(1)
    mtry from missForest function, default NULL.

  • maxiter :: integer(1)
    maxiter from missForest function, default 20.

  • maxnodes :: character(1)
    maxnodes from missForest function, default NULL

  • 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 -> missForest_imputation

Methods

Inherited methods

Method new()

Usage

PipeOpmissForest$new(
  id = "impute_missForest_B",
  cores = NULL,
  ntree_set = c(100, 200, 500, 1000),
  mtry_set = NULL,
  parallel = FALSE,
  mtry = NULL,
  ntree = 100,
  optimize = FALSE,
  maxiter = 20,
  maxnodes = NULL,
  out_file = NULL
)

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpmissForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code
# \donttest{

  # Using debug learner for example purpose

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

  # Task with NA

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

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