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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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287194 |