Practical Convenience Causes Label-Correction Bias in Dev Sets
In a dev set of 1,000 examples with a classifier that has 98.0% accuracy, it is easier to examine the 20 misclassified examples than all 980 correctly classified examples. Because it is easier in practice to check only the misclassified examples, bias does creep into some dev sets.
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Practical Convenience Causes Label-Correction Bias in Dev Sets
When Label-Correction Bias Is Acceptable Versus Problematic
What risk arises when you fix label errors only for the dev-set examples your classifier misclassified?
True or False: Correcting mislabeled dev-set examples only where your system was wrong produces an unbiased evaluation.
Fixing labels only on examples your system _____ can introduce bias into dev-set evaluation.
Why does fixing labels only on misclassified dev examples introduce bias into the evaluation?
Fixing labels only on misclassified dev examples can introduce bias into your evaluation.
To avoid label-correction bias, you should review labels of _____ dev examples, not only misclassified ones.
Match each label-correction practice to its effect on dev set evaluation bias.
Order the steps a team should follow when correcting dev set labels to avoid introducing bias.
What is the most likely effect on measured dev set accuracy when labels are corrected only on misclassified examples?
Reviewing only the dev examples your model misclassified is sufficient to ensure an unbiased dev set evaluation.
Label-correction bias arises because mislabeled examples the system classified _____ are never reviewed or fixed.
Match each term related to label-correction bias to its correct definition.
Order the reasoning steps that explain why fixing only misclassified labels introduces bias into dev set evaluation.
Explain how selective label correction on misclassified examples alters estimated dev set performance.
Evaluating a team's decision to correct only misclassified dev set labels.
State the primary risk of fixing only misclassified dev set labels.