The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems

The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems

Background: The bootstrap can be alternative to cross-validation as a training/test set splitting method since it minimizes the computing time in classification problems in comparison to the tenfold cross-validation.

Bibliographic Details
Main Author: Vrigazova Borislava
Format: Article
Language:English
Published: Sciendo 2021-05-01
Series:Business Systems Research
Subjects:
the bootstrap
classification
cross-validation
repeated train/test splitting
c38
c52
c55
Online Access:https://doi.org/10.2478/bsrj-2021-0015
id doaj-ddd3a9963c8143afbcf375cb40a61180
record_format Article
spelling doaj-ddd3a9963c8143afbcf375cb40a611802021-09-05T21:00:37ZengSciendoBusiness Systems Research1847-93752021-05-0112122824210.2478/bsrj-2021-0015The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification ProblemsVrigazova Borislava0Sofia University, Faculty of Economics and Business Administration, BulgariaBackground: The bootstrap can be alternative to cross-validation as a training/test set splitting method since it minimizes the computing time in classification problems in comparison to the tenfold cross-validation.https://doi.org/10.2478/bsrj-2021-0015the bootstrapclassificationcross-validationrepeated train/test splittingc38c52c55
collection DOAJ
language English
format Article
sources DOAJ
author Vrigazova Borislava
spellingShingle Vrigazova Borislava
The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
Business Systems Research
the bootstrap
classification
cross-validation
repeated train/test splitting
c38
c52
c55
author_facet Vrigazova Borislava
author_sort Vrigazova Borislava
title The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
title_short The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
title_full The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
title_fullStr The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
title_full_unstemmed The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems
title_sort proportion for splitting data into training and test set for the bootstrap in classification problems
publisher Sciendo
series Business Systems Research
issn 1847-9375
publishDate 2021-05-01
description Background: The bootstrap can be alternative to cross-validation as a training/test set splitting method since it minimizes the computing time in classification problems in comparison to the tenfold cross-validation.
topic the bootstrap
classification
cross-validation
repeated train/test splitting
c38
c52
c55
url https://doi.org/10.2478/bsrj-2021-0015
work_keys_str_mv AT vrigazovaborislava theproportionforsplittingdataintotrainingandtestsetforthebootstrapinclassificationproblems
AT vrigazovaborislava proportionforsplittingdataintotrainingandtestsetforthebootstrapinclassificationproblems
_version_ 1717782547789774848

Similar Items