Question: Explain how a machine learning team can use estimates of avoidable bias and variance to choose and prioritize effective changes for their algorithm. In your explanation, detail how the intended effects of techniques addressing bias differ from the intended effects of techniques addressing variance.

Sample answer: A machine learning team can use estimates of avoidable bias and variance, obtained by looking at training and dev set error rates, to understand which component of error is more pressing to address. Developing this intuition helps them choose effective changes for their algorithm. The intended effects of techniques addressing bias are to improve the model's performance on the training set. In contrast, the intended effects of techniques addressing variance are to help the model generalize better from the training set to the dev/test sets. By prioritizing techniques that target the larger component of error, the team avoids applying inappropriate techniques to their project's current problem.

Key points:

• Training and dev set error rates are used to estimate avoidable bias and variance.
• This analysis helps identify which component of error is more pressing to address.
• Techniques addressing bias improve training performance, while techniques addressing variance help generalize from training to dev/test.

Rubric: The response should: 1. Explain that training and dev set error rates are examined to estimate avoidable bias and variance. 2. State that this analysis reveals which error component is more pressing to address. 3. Contrast the intended effect of bias-reducing techniques (improving training set performance) with variance-reducing techniques (improving generalization from training to dev/test sets).