Wrapper around PipeOpSubsample and PipeOpSurvAvg to simplify Graph creation.

## Usage

```
pipeline_survbagging(
learner,
iterations = 10,
frac = 0.7,
avg = TRUE,
weights = 1,
graph_learner = FALSE
)
```

## 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 LearnerSurv.- iterations
`integer(1)`

Number of bagging iterations. Defaults to 10.- frac
`numeric(1)`

Percentage of rows to keep during subsampling. See PipeOpSubsample for more information. Defaults to 0.7.- avg
`logical(1)`

If`TRUE`

(default) predictions are aggregated with PipeOpSurvAvg, otherwise returned as multiple predictions. Can only be`FALSE`

if`graph_learner = FALSE`

.- weights
`numeric()`

Weights for model avering, ignored if`avg = FALSE`

. Default is uniform weighting, see PipeOpSurvAvg.- graph_learner
`logical(1)`

If`TRUE`

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

.

## Details

Bagging (Bootstrap AGGregatING) is the process of bootstrapping data and aggregating
the final predictions. Bootstrapping splits the data into `B`

smaller datasets of a given size
and is performed with PipeOpSubsample. Aggregation is
the sample mean of deterministic predictions and a
MixtureDistribution of distribution predictions. This can be
further enhanced by using a weighted average by supplying `weights`

.

## Examples

```
if (FALSE) { # \dontrun{
if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
library("mlr3")
library("mlr3pipelines")
task = tsk("rats")
pipe = ppl(
"survbagging",
learner = lrn("surv.coxph"),
iterations = 5,
graph_learner = FALSE
)
pipe$train(task)
pipe$predict(task)
}
} # }
```