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Using Human Labels to Approximate the Best Possible Error
When a task is one that people usually do well, such as identifying common objects in images or writing out short audio recordings, you can sample examples, ask a person to label them, and compare the human labels with the established labels for those examples. The resulting human performance gives a practical estimate of the lowest error rate you should expect on that task.
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Using Human Labels to Approximate the Best Possible Error
Human Difficulty Can Make the Best Error Rate Hard to Estimate
Treating the Best Achievable Error as a Bias Floor
Why does the best achievable error matter when separating bias from variance?
The optimal error rate is the lowest achievable error for a task, and model training alone cannot reduce it further.
Choosing a Benchmark for Error Rate
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Order the steps for using an irreducible-error benchmark to check for high bias.
In a machine learning task, what does the lowest possible error for the problem represent?
Hard-to-Estimate Best-Case Error
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Why the best achievable error matters in diagnosis
Estimating the Best Achievable Error in Two Different Applications
What does the best achievable error level mean?
Learn After
Using Human Performance to Estimate the Best Possible Error
What does comparing human labels with the training set estimate?
Human Labels Can Help Estimate the Best Achievable Error Rate on Easy Human Tasks
Estimating the Best Human-Level Error on an Easy Task
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Arrange the steps for estimating the best achievable error rate from human labels.
Which pair of tasks is most suitable for using human labels to estimate a practical lower bound on error?
Human Label Accuracy Is Checked Against the Training Set
Human-Friendly Labeling Tasks
Match each step in the human-label estimate procedure to what it is for.
Order the logic for using human labels as a proxy for the best achievable error rate.
Using Human Labels to Estimate Best-Possible Error
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Human-label tasks for estimating the best possible error