PipeOpSample_B: PipeOpSample_B

In NADIA: NA Data Imputation Algorithms

PipeOpSample_B

Description

Impute features by sampling from non-missing data in approach B (independently during the training and prediction phase).

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 '"impute_sample_B"'.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpImpute -> Sample_B_imputation

Methods

Public methods

• PipeOpSample_B$new()

• PipeOpSample_B$clone()

Inherited methods

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

---

Method new()

Usage

PipeOpSample_B$new(id = "impute_sample_B", param_vals = list())

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpSample_B$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # Task with NA
  set.seed(1)
  resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))
}

NADIA documentation built on Oct. 3, 2022, 1:05 a.m.