Survival Prediction Averaging Pipeline

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.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

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.

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survbagging")
ppl("survbagging")

See also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime

Examples

if (FALSE) { # \dontrun{
  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)
} # }