Your regression model's MSE is high, but the MAE is relatively low. What might this indicate about the model's error distribution, and how would you investigate further?
- Model has consistent errors; needs more training
- Model has frequent large errors; needs regularization
- Model has many small errors, but some significant outliers; analyze residuals
- Model is perfect; no further investigation required
A high Mean Squared Error (MSE) with a relatively low Mean Absolute Error (MAE) indicates that the model likely has many small errors but also some significant outliers. The squaring in MSE amplifies the effect of these outliers. Analyzing the residuals (the differences between predicted and actual values) can help to understand the nature of these errors and possibly guide improvements in the model.
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