PipeOpmissForest {NADIA} | R Documentation |
PipeOpmissForest
Description
Implements missForest methods as mlr3 pipeline more about missForest autotune_missForest
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, defaultNULL
. -
ntree_set
::integer(1)
Vector with number of trees values for grid search, used only when optimize=TRUE, defaultc(100,200,500,1000)
. -
mtry_set
::integer(1)
Vector with number of variables values randomly sampled at each split, used only when optimize=TRUE, defaultNULL
. -
parallel
::logical(1)
If TRUE parallel calculations are used, defaultFALSE
. -
ntree
::integer(1)
ntree from missForest function, default100
. -
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, defaultFALSE
. -
mtry
::integer(1)
mtry from missForest function, defaultNULL
. -
maxiter
::integer(1)
maxiter from missForest function, default20
. -
maxnodes
::character(1)
maxnodes from missForest function, defaultNULL
-
out_fill
::character(1)
Output log file location. If file already exists log message will be added. If NULL no log will be produced, defaultNULL
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> missForest_imputation
Methods
Public 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
# 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))