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How Informal Bias Relates to Training Error
Question: In an informal discussion of machine learning, how is bias described, and why does the size of the training set matter?
Sample answer: A common informal description of bias is the model's error on the training data. When the training set is very large, that training error is used as a rough approximation of the model's bias.
Key points:
- Bias is described informally as training error.
- With a very large training set, training error serves as a rough stand-in for bias.
Rubric: The response must say that bias is informally described by the model's error on the training data, and that this is only a rough approximation when the training set is very large.
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