Which error comparison most directly reveals the data mismatch problem in the example with 1% train, 1.5% same-dist, and 10% dev error?

The error pattern (1% train, 1.5% same-dist unseen, 10% dev) primarily indicates a high variance (overfitting) problem.

In the data mismatch example, the algorithm achieves _____ error on the dev set.

Match each error measurement in the data mismatch example to its correct value.

Order the analytical steps used to diagnose the data mismatch problem in the Machine Learning Yearning example.

What does the 0.5% gap between training error (1%) and same-distribution unseen error (1.5%) indicate in the data mismatch example?

In the data mismatch example, the 1% training error vs. near-perfect human performance represents the most significant problem to address.

In the data mismatch example, error on unseen data drawn from the same distribution as the training set is _____.

Match each error comparison in the data mismatch example to the type of problem it diagnoses.

Order the observations that build the case that data mismatch — not overfitting or underfitting — is the dominant problem in the Machine Learning Yearning example.