Sep 30, 2023 · Both collider bias and confound-leakage describe situations where variable adjustment can lead to spurious relationships between features and ...
Oct 17, 2022 · We demonstrate the real-world importance of confound-leakage by analyzing a clinical dataset where accuracy is overestimated for predicting ...
Conclusions: Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, ...
Oct 2, 2023 · We demonstrate the real-world importance of confound-leakage by analyzing a clinical dataset where accuracy is overestimated for predicting ...
Oct 17, 2022 · Confound-leakage would be detrimental to the validity and interpretability of the. CR-ML workflow as the model and its predictions would be ...
Jan 18, 2023 · We demonstrate the real-world importance of confound-leakage by analyzing a clinical dataset where accuracy is overestimated for predicting ...
Jul 10, 2023 · Confound-leakage: confound removal in machine learning leads to leakage. Gigascience. 2022 Dec 28:12:giad071. doi: 10.1093/gigascience/giad071.
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Lastly, there is also some evidence that removing confounds can leak information about the target into the features, biasing the resulting predictive models [14] ...
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage · Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of ...
This work provides a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines and expands understanding associated ...