PipeOpmissRanger {NADIA} | R Documentation |
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, default500
-
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, default5
. -
random.seed
::integer(1)
Random seed, default123
. -
iter
::integer(1)
Number of iterations for a random search, default10
. -
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
. -
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
-> missRanger_imputation
Methods
Public methods
Inherited methods
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
# 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))