Measuring Human Error on Mobile-Image Examples
Human-level performance on mobile images can be measured by asking humans to label the mobile cat-image data and measuring their error.
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Measuring Human Error on Mobile-Image Examples
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Why is the optimal error rate for cat recognition nearly 0% according to Machine Learning Yearning?
If 14% of audio clips are too noisy for humans to understand, the optimal speech recognition error rate is approximately 14%.
Human-level performance is used as a proxy to estimate the _____ error rate on a given task.
Match each task scenario to the approximate optimal error rate implied by human-level performance.
Order the steps for using human-level performance to estimate optimal error rate and guide bias reduction.
An algorithm achieves 10% error on a task where humans achieve 2% error. What is the avoidable bias and what action does this suggest?
Human-level performance always equals 0% error, so the optimal error rate is always 0% for any machine learning task.
In the cat recognition example, because a human can recognize cats almost all the time, the ideal error rate is nearly _____.
Match each key term to its definition in the context of human-level performance as an optimal error rate proxy.
Order the reasoning steps for deciding whether a task's optimal error rate is near 0% or substantially higher.