Remedying an Overfit Eyeball Dev Set

What is the primary signal that your Eyeball dev set has been overfit during manual error analysis?

Manually examining Eyeball dev set examples causes you to overfit that set faster than if you had not examined them.

Explicitly splitting the dev set into Eyeball and Blackbox subsets allows you to detect when _____ is causing overfitting of the Eyeball portion.

Match each dev set concept to its correct description in the Eyeball/Blackbox framework.

Order the steps for detecting Eyeball dev set overfitting using the Blackbox dev set as a benchmark.

After several rounds of error analysis, your Eyeball dev set accuracy is 92% while your Blackbox dev set accuracy is 78%. What does this most likely indicate?

In the Eyeball/Blackbox framework, examples in the Blackbox dev set are regularly reviewed manually during error analysis.

If performance on the Eyeball dev set improves much more rapidly than on the Blackbox dev set, you have _____ the Eyeball dev set.

Match each observed performance pattern to its correct interpretation in the Eyeball/Blackbox framework.

Order the reasoning steps used to decide whether the Eyeball dev set has been overfit and what action to take.

Explain the mechanism of Eyeball dev set overfitting and how comparing it to a Blackbox dev set detects this issue.

Diagnosing divergent performance between Eyeball and Blackbox dev sets in a speech recognition system.

How does splitting the dev set help evaluate the manual error analysis process?