Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/224677
Authors: 
Vrigazova, Borislava
Year of Publication: 
2020
Citation: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020 [ISSN:] 2706-4735 [Volume:] 6 [Pages:] 74-83
Abstract: 
In this research, we propose the bootstrap procedure as a method for train/test splitting in machine learning algorithms for classification. We show that this resampling method can be a reliable alternative to cross validation and repeated random test/train splitting algorithms. The bootstrap procedure optimizes the classifier's performance by improving its accuracy and classification scores and by reducing computational time significantly. We also show that ten iterations of the bootstrap procedure are enough to achieve better performance of the classification algorithm. With these findings, we propose a solution to the problem of how to reduce computing time in large datasets, while introducing a new practical application of the bootstrap procedure.
Subjects: 
the bootstrap
cross validation
repeated train/test splitting
JEL: 
C38
C52
C55
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/4.0/
Document Type: 
Conference Paper

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