Imagine you're dealing with a classification model. The dataset has a significant amount of missing data that was replaced with the mean. How could this decision have impacted the model's performance?
- It could distort the feature's statistical properties.
- It could increase the model's accuracy.
- It could lead to overfitting.
- It could lead to underfitting.
Replacing missing data with the mean can distort the feature's statistical properties (like variance), which could affect the model's learning and prediction capability.
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