mlr_graphs_survbagging | R Documentation |
Wrapper around PipeOpSubsample and PipeOpSurvAvg to simplify Graph creation.
pipeline_survbagging(
learner,
iterations = 10,
frac = 0.7,
avg = TRUE,
weights = 1,
graph_learner = FALSE
)
learner |
|
iterations |
( |
frac |
( |
avg |
( |
weights |
( |
graph_learner |
( |
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
.
mlr3pipelines::Graph or mlr3pipelines::GraphLearner
This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():
mlr_graphs$get("survbagging") ppl("survbagging")
Other pipelines:
mlr_graphs_crankcompositor
,
mlr_graphs_distrcompositor
,
mlr_graphs_probregr
,
mlr_graphs_responsecompositor
,
mlr_graphs_survaverager
,
mlr_graphs_survtoclassif_IPCW
,
mlr_graphs_survtoclassif_disctime
,
mlr_graphs_survtoregr_pem
## Not run:
library(mlr3)
library(mlr3pipelines)
task = tsk("rats")
pipe = ppl(
"survbagging",
learner = lrn("surv.coxph"),
iterations = 5,
graph_learner = FALSE
)
pipe$train(task)
pipe$predict(task)
## End(Not run)
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