What does a 1% gap between training error (14%) and training-dev error (15%) suggest?

True or False: An algorithm with 8% training error, 9% training-dev error, and 18% dev error is showing high variance on the training-set distribution.

High avoidable bias with a distribution shift

Match each error gap in the 8%/9%/17% scenario to the machine learning problem it diagnoses.

Diagnosing Bias, Variance, and Distribution Shift from Error Rates

True or False: If training error is 8%, training-dev error is 9%, and dev error is 15%, the mismatch between training-dev and dev explains more of the drop than variance does.

Estimating Variance with a Held-Out Same-Source Set

Match each diagnosis to the evidence in the 12%/13%/19% scenario.

Order the reported error rates from lowest to highest in a case where the model fits the training set well but struggles on a shifted dev set.

Interpreting training, in-domain, and deployment errors

Diagnosing Bias and Distribution Shift

What problem is ruled out?