mlr_graphs_bagging {mlr3pipelines} | R Documentation |
Create a bagging learner
Description
Creates a Graph
that performs bagging for a supplied graph.
This is done as follows:
-
Subsample
the data in each step usingPipeOpSubsample
, afterwards applygraph
Replicate this step
iterations
times (in parallel via multiplicities)Average outputs of replicated
graph
s predictions using theaverager
(note that settingcollect_multipliciy = TRUE
is required)
All input arguments are cloned and have no references in common with the returned Graph
.
Usage
pipeline_bagging(
graph,
iterations = 10,
frac = 0.7,
averager = NULL,
replace = FALSE
)
Arguments
graph |
|
iterations |
|
frac |
|
averager |
|
replace |
|
Value
Examples
library(mlr3)
lrn_po = po("learner", lrn("regr.rpart"))
task = mlr_tasks$get("boston_housing")
gr = pipeline_bagging(lrn_po, 3, averager = po("regravg", collect_multiplicity = TRUE))
resample(task, GraphLearner$new(gr), rsmp("holdout"))$aggregate()
# The original bagging method uses boosting by sampling with replacement.
gr = ppl("bagging", lrn_po, frac = 1, replace = TRUE,
averager = po("regravg", collect_multiplicity = TRUE))
resample(task, GraphLearner$new(gr), rsmp("holdout"))$aggregate()