A sled-dog rest-stop model posts consistently low accuracy, precision, recall, or F1 on both the training set and the validation set. How should testers read that pair?
Select an answer to reveal the explanation.
Short Explanation
Consistently low accuracy, precision, recall, or F1 on both the training set and the validation set is underfitting, not a later operational-drift story. Overfitting would look strong on the fit pile and weak later. A low F1 is not a distribution-shape watch, and a later drift tale will not fix both piles.
Full Explanation
Consistently poor ML functional-performance metrics on both training and validation indicate underfitting. Reading the pair as overfitting, as static drift, or as a later operational-drift story misses that detection.