apply some Gigascience copyeditor changes #75
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dhimmel
commented
Jan 12, 2024
| While predictions based primarily on degree may be acceptable for some tasks, generating less obvious insights from networks requires drawing inferences from the specific connections and network structure between nodes. | ||
| Model evaluation is challenging in this context: nonspecific or trivial predictions can dominate performance evaluations and may actually be correct, even if they are not the desired outputs of the predictive model. | ||
| For example, predicting that the highest degree node in a network shares edges with the remaining nodes to which it is not connected will often lead to many correct predictions, despite this prediction being generic to all other nodes in the network. | ||
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| Degree is important in edge prediction, but it can cause undesired effects. | ||
| Degree-based features should often be included in the interpretation of predictions to disentangle desired from non-desired effects and to effectively evaluate and compare predictive models. | ||
| Degree-based features should often be included in the interpretation of predictions to disentangle desired from undesired effects and to effectively evaluate and compare predictive models. |
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we might want to make this change (nondesired --> undesired) in the Gigascience version as well
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AppVeyor build 1.0.82 |
zietzm
approved these changes
Jan 12, 2024
Co-authored-by: Michael Zietz <michael.zietz@gmail.com>
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[ci skip] This build is based on 084aa98. This commit was created by the following CI build and job: https://github.com/greenelab/xswap-manuscript/commit/084aa987338d038f11579641fa4b9e30e6e74826/checks https://github.com/greenelab/xswap-manuscript/actions/runs/7506777009
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[ci skip] This build is based on 084aa98. This commit was created by the following CI build and job: https://github.com/greenelab/xswap-manuscript/commit/084aa987338d038f11579641fa4b9e30e6e74826/checks https://github.com/greenelab/xswap-manuscript/actions/runs/7506777009
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from #69 (comment)