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