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Human-level error** gives an estimate of Bayes error.

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Example 1: Medical image classification
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This is an example of a medical image classification in which the input is a radiology image and the output
is a diagnosis classification decision.

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The definition** of human-level error depends on the purpose of the analysis, in this case, by definition the
Bayes error is lower or equal to 0.5%.

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Example 2: Error analysis
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Scenario A**
In this case, the choice of human-level performance doesn’t have an impact. The avoidable bias is between
4%-4.5% and the variance is 1%. Therefore, the focus should be on bias reduction technique.

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Scenario B**
In this case, the choice of human-level performance doesn’t have an impact. The avoidable bias is between
0%-0.5% and the variance is 4%. Therefore, the focus should be on variance reduction technique.

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Scenario C**
In this case, the estimate for Bayes error has to be 0.5% since you can’t go lower than the human-level
performance otherwise the training set is overfitting. Also, the avoidable bias is 0.2% and the variance is
0.1%. Therefore, the focus should be on bias reduction technique.

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Summary** of bias/variance with human-level performance

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Human - level error – proxy for Bayes error
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If the difference between human-level error and the training error is bigger than the difference
between the training error and the development error. The focus should be on bias reduction
technique

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If the difference** between training error and the development error is bigger than the difference
between the human-level error and the training error. The focus should be on variance reduction
technique