Human Labeling Works Best for Tasks People Can Do Reliably

Using Human Judgment in Error Review

Use Human-Strong Subsets to Keep Improving

Which set of advantages best explains why comparing with human performance can help ML work?

Comparing a model with strong human performance can help estimate the lowest achievable error and set a realistic target for improvement.

A realistic target error rate can speed up a team’s ____.

Match each reason for comparing against human performance with its use in machine learning work.

Order the reasoning steps for using a human benchmark in model development.

Why comparing algorithm performance to expert performance helps ML teams improve

What to do when a model trails expert performance by a wide margin

Why does a large gap from human performance matter in model debugging?

What choice would most help a team move quickly during model development?

Human-level comparison is useful only for collecting labels.