What are some potential issues when using MSE, and how can they be addressed?

MSE is sensitive to outliers because of the squaring operation. A single large error can significantly inflate the MSE. To mitigate this, consider using robust loss functions like Mean Absolute Error (MAE) or Huber loss, which are less affected by outliers. Data preprocessing techniques like outlier removal or transformation can also help.