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Use the Strongest Practical Human Benchmark as a Proxy

If more than one human reference point is available, it is usually best to take the strongest realistic human or human-team result as the stand-in for near-optimal performance. For example, if a group of physicians makes 2% errors on an imaging task, that 2% rate is a sensible benchmark for the target error rate. A looser target such as 5% or 10% would be too weak, because the human team already does better.

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Updated 2026-08-12

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Machine Learning

Deep Learning

Supervised Learning

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Data Science

Machine Learning Strategy

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