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hyperSMURF (version 2.0)

do.stratified.cv.data.from.folds: Construction of folds for cross-validation from predefined folds

Description

The function generates data for cross-validation from pre-computed folds

Usage

do.stratified.cv.data.from.folds(examples, positives, folds, k = 10)

Arguments

examples

vector of integer: indices of the examples

positives

vector of integer: Indices of the positive examples. The indices refer to the indices of examples

folds

vector of indices : its length is equal to examples, with values in the interval \([0,kk)\). The value indicates the partition in the cross validation step of the class

k

number of folds (def = 10)

Value

a list with two components;

fold.non.positives

a list with k components. Each component is a vector with the indices of the non positive elements of the fold

old.positives

a list with k components. Each component is a vector with the indices of the positive elements of the fold

Details

The folds are separated for positive and negative examples. The elements included in each fold are obtained from the vector of fold indices folds.

See Also

do.stratified.cv.data

Examples

Run this code
# NOT RUN {
do.stratified.cv.data.from.folds(1:100, 1:10, folds=sample(rep((0:4),20)), k = 5)
# }

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