Question: Based on Andrew Ng's Machine Learning Yearning course, define what "mislabeled" means when inspecting a dev set, specify which component of the data point (x, y) is affected, and provide a concrete example of how this occurs.

Sample answer: In the context of dev set error analysis, "mislabeled" refers to examples that were assigned incorrect labels by a human labeler before the machine learning algorithm processed them. In a labeled example (x, y), this means the class label y has an incorrect value. For example, a picture that does not contain a cat might be incorrectly labeled as containing a cat, or a picture that does contain a cat might be labeled as not containing one.

Key points:

• Mislabeled examples are incorrect due to human labeler error before the algorithm encounters them.
• In a data point (x, y), the class label y has an incorrect value.
• Pictures not containing a cat may be labeled as a cat, or vice versa.

Rubric: The answer should define "mislabeled" as an error introduced by human labelers before the algorithm encounters the data. It must specify that the class label y in (x, y) has the incorrect value. It must also provide an example of mislabeling (e.g., cat vs. non-cat pictures).