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Explain the Relationship Between Informal Bias and Training Set Size
Question: Discuss how Machine Learning Yearning defines bias informally. Specifically, explain the role that the size of the training set plays in this informal definition.
Sample answer: According to Machine Learning Yearning, an algorithm's bias is informally defined as its error rate on the training set. Roughly, bias is the error rate of the algorithm on the training set specifically when the training set is very large.
Key points:
- Informally, an algorithm's bias is its error rate on the training set.
- Roughly, bias is the training set error rate when the training set is very large.
Rubric: The response must state that bias is defined informally as the training set error rate, and explain that this approximation holds when the training set is very large.
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What does an algorithm's bias informally measure according to Machine Learning Yearning?
True or False: In Machine Learning Yearning, an algorithm's informal bias is defined as its error rate on the dev/test set.
Informally, an algorithm's _____ is its error rate on the training set when the training set is very large.
What does 'bias' informally refer to according to Machine Learning Yearning?
Bias is informally defined as the algorithm's error rate on the training set.
Informally, an algorithm's _____ is its error rate on the training set.
Match each term to its description in Machine Learning Yearning's bias/variance framework.
Order the steps to correctly estimate an algorithm's bias according to Machine Learning Yearning.
Why does Machine Learning Yearning qualify bias as the training error rate on a 'very large' training set?
In Machine Learning Yearning, bias is defined as the algorithm's error rate on the dev or test set.
Roughly, the bias is the error rate of your algorithm on your _____ set when you have a very large training set.
Match each aspect of the bias definition to what it represents in Machine Learning Yearning.
Order the reasoning steps to determine whether bias is the primary error source in an underperforming ML algorithm.
Explain the Relationship Between Informal Bias and Training Set Size
Evaluating Training Error as a Measure of Bias
Distinguishing the Informal Definition of Bias