PipeOpSimulateMissings {NADIA} | R Documentation |
PipeOpSimulateMissings
Description
Generates MCAR missing values in mlr3 pipeline according to set parameters. Missings are inserted to task data once during first training.
Input and Output Channels
Input and output channels are inherited from PipeOpTaskPreproc
.
Parameters
-
per_missings
::double(1)
Overall percentage of missing values generated in dataset [0, 100]. Must be set every time, default 50 -
per_instances_missings
::double(1)
Percentage of instances which will have missing values [0, 100]. -
per_variables_missings
::double(1)
Percentage of variables which will have missing values [0, 100]. -
variables_missings
::integer
Only when 'per_variables_missings' is 'NULL'. Vector of indexes of columns in which missings will be generated.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpTaskPreproc
-> PipeOpSimulateMissings
Methods
Public methods
Inherited methods
Method new()
Usage
PipeOpSimulateMissings$new( id = "simulate_missings", param_vals = list(per_missings = 50) )
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpSimulateMissings$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
{
task_NA <- PipeOpSimulateMissings$new()$train(list(tsk("iris")))[[1]]
# check
sum(task_NA$missings()) > 0
}