Bootstrap-class {performanceEstimation}

Class "Bootstrap"

Description

This class of objects contains the information describing a bootstrap experiment, i.e. its settings.

Objects from the Class

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.

Slots

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.

Extends

Class EstCommon, directly. Class EstimationMethod, directly.

Methods

show

signature(object = "Bootstrap"): method used to show the contents of a Bootstrap object.

Author(s)

Luis Torgo [e-mail]

References

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

See Also

MonteCarlo, LOOCV, CV, Holdout, EstimationMethod, EstimationTask

Examples

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