At 28% system error, which benchmark is most useful for deciding where to improve next?
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How Human Performance Guides Machine Learning Work
At 28% system error, which benchmark is most useful for deciding where to improve next?
If a classifier has about 40% error, using a nurse practitioner with 12% error instead of a senior specialist with 6% error as the human reference makes only a small practical difference.
Using a human benchmark when error is already fairly low
Match each model error situation to the lesson it gives about choosing a human benchmark.
Order the steps for deciding when a more precise human benchmark is worth using.
Why is a 2% human benchmark more useful for guiding improvement when a system has 10% error than when it has 40% error?
At a 40% system error rate, switching between a 12% human benchmark and a 6% human benchmark usually changes the diagnosis a great deal.
When a model is meant to match expert inspectors, their error rate can be the _____ error rate for the system.
Match each labeler or system case to its description.
Order the steps for choosing a human performance reference when evaluating an ML system.
How should the choice of human reference change as a model gets better?
Select the right human reference when system error is very high.
Why a tighter human reference helps when error is already low