Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Schulz et al. systematically benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets. Complicated nonlinear intervariable relationships remain largely inaccessible for predicting key phe...
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| Main Authors: | , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/b1ee0d1de11c40d8869725556c90d89f |
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| Summary: | Schulz et al. systematically benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets. Complicated nonlinear intervariable relationships remain largely inaccessible for predicting key phenotypes from typical brain scans. |
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