Suppose you have a model with a high level of precision but low recall. You notice that missing data was handled incorrectly. How might this have affected the model's performance?
- Missing data could have affected the model's complexity.
- Missing data might have introduced false negatives.
- Missing data might have introduced false positives.
- Missing data might have skewed the distribution of the data.
Incorrect handling of missing data may result in the model being trained on a biased dataset, leading to false negatives and subsequently a lower recall.
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