mlr_graphs_survbagging: Survival Prediction Averaging Pipeline
In mlr3proba: Probabilistic Supervised Learning for 'mlr3'

mlr_graphs_survbagging

R Documentation

Survival Prediction Averaging Pipeline

Description

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.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

mlr3pipelines::GraphLearner

Examples

## Not run: 
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)
}

## End(Not run)

mlr3proba documentation built on April 25, 2022, 5:07 p.m.