Consider you have a regression model that is underfitting. On investigation, you discover missing data was dropped instead of imputed. What might be the reason for underfitting in this context?
- The model didn't have enough data to learn from.
- The model was over-regularized.
- The model's complexity was too low.
- The model's hyperparameters were not optimized.
Dropping missing data can significantly reduce the size of the training set. If much of the data is discarded, the model may not have enough data to learn the underlying patterns, leading to underfitting.
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