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Distinguishing the Informal Definition of Bias
Question: How is an algorithm's bias informally defined in Machine Learning Yearning, and under what condition does this approximation roughly hold?
Sample answer: Informally, an algorithm's bias is defined as the algorithm's error rate on the training set. This approximation roughly holds when the training set is very large.
Key points:
- Bias is informally defined as the algorithm's error rate on the training set.
- This definition roughly holds when the training set is very large.
Rubric: The answer should identify that bias is informally defined as the error rate on the training set, and specify that this is 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