A brine-temperature regressor posts a large RMSE on the training slice and a similarly large RMSE on validation, and both lose to a seasonal mean baseline. What does that pattern mean?
Select an answer to reveal the explanation.
Short Explanation
The brine-temperature regressor posts a large RMSE on both slices and loses to a seasonal mean. That is underfitting. Two large RMSEs are not memorization, Fraud Detector is not that diagnosis, and losing to the baseline is not a win.
Full Explanation
Underfitting shows as high error on both train and validation relative to a sensible baseline. Two large RMSEs that lose to a seasonal mean are not overfitting. Fraud Detector is not that diagnosis, and losing to the baseline is not a healthy fit.