An herbarium model memorizes last decade’s catalog notes and then fails on this year’s new sheets, while a second model is so simple it misses obvious moisture patterns even on the training years. What are those two failures?
Select an answer to reveal the explanation.
Short Explanation
The catalog-note memorizer is overfitting: strong on train, poor on new sheets. The model that misses moisture even on training years is underfitting. Swap those labels and the trap snaps.
Full Explanation
Overfitting memorizes training examples and fails on new data. Underfitting is too simple to capture the pattern even on the training set. Swapping those labels, calling both healthy generalization, or blaming GPU cooling misses the contrast.