Can normalization methods allow escape from the doppelgänger effect in biomedical data?
The Doppelganger Effect (DE) describes the situation when an AI/ML model performs well on a validation set regardless of whether it has truly learned. DE may exaggerate the reported performance of the AI/ML model on real-world data, complicate model selection processes and lead towards false domain...
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| 格式: | Thesis-Master by Research |
| 語言: | English |
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Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/165285 |
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