Reviewing Only Mistakes Can Skew a Dev Set

When Selective Relabeling Is Acceptable

What problem can occur if you correct only the dev-set examples your model got wrong?

True or False: If you relabel only the validation examples your system missed, the validation result remains an unbiased estimate.

Fixing labels only on the development examples your model misclassifies can introduce bias into evaluation because those examples are not selected at random.

How can selective relabeling skew a development-set score?

Editing Only the Wrongly Labeled Dev Examples Can Skew Your Evaluation

To avoid label-fix bias, review labels on _____ dev examples, not only the ones your model got wrong.

Match each review policy to its impact on validation-set fairness.

Put the label-review workflow in the right order to avoid bias.

What happens to the measured development accuracy if labels are corrected only for examples the model got wrong?

Checking only the dev examples your model got wrong is enough to guarantee an unbiased evaluation set.

Selective relabeling misses examples the classifier gets _____, so some bad labels are never examined.

Match each label-audit term with its meaning.

Put the steps in order to show why correcting only mislabeled mistakes can distort validation results.

Why does correcting only the labels of examples the model got wrong distort measured accuracy?

Why reviewing only mistaken labels can distort validation results

State the main risk of fixing only misclassified dev set labels.