Question: Analyze the trade-offs of using model size reduction as a method to address high variance in a machine learning model, and compare its effectiveness and primary benefits to adding regularization.

Sample answer: Decreasing model size (such as reducing neurons or layers) can decrease variance, but it may also increase bias. It is not generally recommended for reducing variance because adding regularization typically yields better classification performance. The main advantage of a smaller model is that it reduces computational cost, thereby speeding up training. Therefore, if computational cost is not a concern, regularization is preferred to address variance; reducing model size is only justified when faster training is a key requirement.

Key points:

• Decreasing model size can reduce variance but is likely to increase bias.
• Regularization is preferred over model size reduction because it yields better classification performance.
• The primary benefit of a smaller model is reduced computational cost and faster training speed.
• Decreasing model size is not recommended as a variance remedy if computational cost is not a concern.

Rubric: The response must explain that decreasing model size reduces variance at the cost of increasing bias. It must compare this method to regularization, noting that regularization provides better classification performance. Finally, it must identify that the main benefit of reducing model size is faster training and lower computational cost, which justifies its use only when training speed is a priority.