Human-Level Performance as a Proxy for Optimal Error Rate
In cat recognition, because a human can recognize whether a picture contains a cat almost all the time, the ideal error rate achievable by an optimal classifier is nearly 0%. In a speech-recognition task where 14% of audio clips are too noisy or unintelligible for even a human to recognize, even the most optimal speech recognition system might have error around 14%.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
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Supervised Learning
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Related
Human-Level Performance as a Proxy for Optimal Error Rate
What does measuring human label accuracy relative to the training set give you, according to Machine Learning Yearning?
Tasks that humans are reasonably good at, such as recognizing pictures, are suitable for estimating the optimal error rate via human labels.
To estimate the optimal error rate on a human-friendly task, you ask a human to provide _____ and measure their accuracy relative to the training set.
Match each concept to its definition in Machine Learning Yearning's method of estimating optimal error rate via human labels.
Arrange the steps for estimating the optimal error rate via human labels in the correct order described in Machine Learning Yearning.
Which pair of tasks does Machine Learning Yearning explicitly cite as examples where human labels can estimate the optimal error rate?
In Machine Learning Yearning's method, human label accuracy is measured relative to the test set to estimate the optimal error rate.
According to Machine Learning Yearning, recognizing pictures and transcribing _____ are examples of tasks humans are reasonably good at.
Match each action in the human-label estimation method to its purpose, as described in Machine Learning Yearning.
Arrange the reasoning steps in correct logical order for why human labels on human-friendly tasks can estimate the optimal error rate.
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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.