Impute features by sampling from non-missing training data.
PipeOpImputeSample$new(id = "imputesample", param_vals = list())
Identifier of resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
Input and output channels are inherited from
The output is the input
Task with all affected numeric features missing values imputed by values sampled (column-wise) from training data.
$state is a named
list with the
$state elements inherited from
$state$model is a named
list of training data with missings removed.
The parameters are the parameters inherited from
sample() function. Features that are entirely
NA are imputed as
the following: For
ordered, random levels are sampled uniformly at random.
FALSE are sampled uniformly at random.
Numerics and integers are imputed as
Other Imputation PipeOps:
library("mlr3") task = tsk("pima") task$missings() #> diabetes age glucose insulin mass pedigree pregnant pressure #> 0 0 5 374 11 0 0 35 #> triceps #> 227 po = po("imputesample") new_task = po$train(list(task = task))[] new_task$missings() #> diabetes age pedigree pregnant glucose insulin mass pressure #> 0 0 0 0 0 0 0 0 #> triceps #> 0