How could the handling of missing data influence the interpretability of a machine learning model?
- Depends on the model used.
- Does not impact model interpretability.
- Makes the model less interpretable.
- Makes the model more interpretable.
If missing data are handled incorrectly, it may lead to inaccurate learning and prediction, which makes the model's decisions less understandable and hence reduces its interpretability.
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