Case context: You are developing a cat classifier. During error analysis of the dev set, you notice a picture of a dog that the algorithm correctly predicted as "not cat", but it was marked as an error because the target label was set to "cat".

Question: Diagnose the nature of this error. What is this type of issue called, who caused it, and what specific part of the data point (x, y) is incorrect?

Sample answer: This issue is a "mislabeled" example. The error was caused by a human labeler who assigned the label before the algorithm encountered the picture. Specifically, in the data point (x, y), the class label y was set to "cat" (incorrect value) instead of "not cat", while the input x is the picture of the dog.

Key points:

• Identify the issue as a "mislabeled" example.
• The error was introduced by a human labeler before the algorithm encountered the data.
• The class label y in (x, y) has an incorrect value.

Rubric: The candidate must identify the issue as a "mislabeled" example. They must state that the error was made by a human labeler before the algorithm encountered it. They must specify that the class label y is the part of the data point (x, y) that has the incorrect value.