A Comparative study of data splitting algorithms for machine learning model selection

A Comparative study of data splitting algorithms for machine learning model selection

Data splitting is commonly used in machine learning to split data into a train, test, or validation set. This approach allows us to find the model hyper-parameter and also estimate the generalization performance. In this research, we conducted a comparative analysis of different data partitioning al...

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Bibliographic Details
Main Author: Birba, Delwende Eliane
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2020
Subjects:
K-fold
cross-validation
Kennard-Stone algorithm
data splitting
bootstrap
overfitting
SPXY
k-faldig korsvalidering
korsvalidering
Kennard-Stone-algoritm
datapartitionering
överanpassning
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287194

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287194

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