Suppose you have an overfitting model. You identify that missing data was incorrectly filled with a constant value. How might this have contributed to overfitting?
- The model became too complex.
- The model learned noise from the data.
- The model was under-regularized.
- The model's hyperparameters were not optimized.
Filling missing data with a constant value could introduce noise into the data, causing the model to learn the noise along with the underlying patterns, thus leading to overfitting.
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