Question: Explain what it means for older learning algorithms, such as logistic regression, to "plateau" as more data is added. Why is this a limitation?

Sample answer: When older learning algorithms like logistic regression "plateau," it means that their performance stops improving regardless of how much additional training data is provided. Their learning curves "flatten out." This is a significant limitation today because we have massive amounts of data available, and these older algorithms essentially do not have the capacity to learn from or effectively utilize this vast surplus of information.

Key points:

• Plateauing means performance stops improving with more data.
• The learning curve flattens out.
• An example is logistic regression.
• Older algorithms cannot effectively utilize the massive amounts of data available today.

Rubric: The essay should define the term "plateau" as the flattening of the learning curve and the cessation of performance improvement. It should also note the inability of older algorithms to utilize large modern datasets.