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.

Order the diagnostic steps for identifying high bias and data mismatch without high variance.

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?