Wrapper around PipeOpProbregr to simplify Graph creation.

## Arguments

- learner
`[mlr3::Learner]|[mlr3pipelines::PipeOp]|[mlr3pipelines::Graph]`

Either a`Learner`

which will be wrapped in mlr3pipelines::PipeOpLearner, a`PipeOp`

which will be wrapped in mlr3pipelines::Graph or a`Graph`

itself. Underlying`Learner`

should be LearnerRegr.- learner_se
`[mlr3::Learner]|[mlr3pipelines::PipeOp]`

Optional LearnerRegr with predict_type`se`

to estimate the standard error. If left`NULL`

then`learner`

must have`se`

in predict_types.- dist
`character(1)`

Location-scale distribution to use for composition. Current possibilities are'`"Cauchy", "Gumbel", "Laplace", "Logistic", "Normal", "Uniform"`

. Default is`"Uniform"`

.- graph_learner
`logical(1)`

If`TRUE`

returns wraps the Graph as a GraphLearner otherwise (default) returns as a`Graph`

.

## Examples

```
if (FALSE) {
if (requireNamespace("mlr3pipelines", quietly = TRUE) &&
requireNamespace("rpart", quietly = TRUE)) {
library("mlr3")
library("mlr3pipelines")
task = tsk("boston_housing")
# method 1 - same learner for response and se
pipe = ppl(
"probregr",
learner = lrn("regr.featureless", predict_type = "se"),
dist = "Uniform"
)
pipe$train(task)
pipe$predict(task)
# method 2 - different learners for response and se
pipe = ppl(
"probregr",
learner = lrn("regr.rpart"),
learner_se = lrn("regr.featureless", predict_type = "se"),
dist = "Normal"
)
pipe$train(task)
pipe$predict(task)
}
}
```