Description Objects from the Class Slots Extends Methods Author(s) References See Also Examples
This class of objects contains the information describing a bootstrap experiment, i.e. its settings.
Objects can be created by calls of the form Bootstrap(...)
providing the values for the class slots.
The objects contain information on the type of boostrap, the number of
repetitions, the random number generator seed
and optionally the
concrete data splits to use on each iteration of the boostrap
experiment. Note that most of the times you will not supply these data
splits as the boostrap routines in this infra-structure will take care of
building them. Still, this allows you to replicate some experiment
carried out with specific train/test splits.
type
:Object of class character
indicating
the type of boostrap estimates to use: "e0" (default) or ".632".
nReps
:Object of class numeric
indicating
the number of repetitions of the bootstrap experiment (defaulting
to 200).
seed
:Object of class numeric
with the
random number generator seed (defaulting to 1234).
dataSplits
:Object of class list
containing the data splits to use on each bootstrap
repetition. Each element should be a list with two components:
test
and train
, on this order. Each of these is a
vector with the row ids to use as test and train sets of each
repetition of the bootstrap experiment.
Class EstCommon
, directly.
Class EstimationMethod
, directly.
signature(object = "Bootstrap")
: method used to
show the contents of a Bootstrap
object.
Luis Torgo ltorgo@dcc.fc.up.pt
Torgo, L. (2014) An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R. arXiv:1412.0436 [cs.MS] http://arxiv.org/abs/1412.0436
MonteCarlo
,
LOOCV
,
CV
,
Holdout
,
EstimationMethod
,
EstimationTask
1 2 3 4 5 6 7 8 9 10 11 12 | showClass("Bootstrap")
s <- Bootstrap(type=".632",nReps=400)
s
## Small example illustrating the format of user supplied data splits
s2 <- Bootstrap(dataSplits=list(list(test=sample(1:150,50),train=sample(1:150,50)),
list(test=sample(1:150,50),train=sample(1:150,50)),
list(test=sample(1:150,50),train=sample(1:150,50))
))
s2
s2@dataSplits
|
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