Question: Explain what a learning curve looks like when an algorithm has both high bias and high variance, and what general conclusion must be drawn about next steps for the algorithm.

Sample answer: The learning curve will show a training error that is large, meaning it is much higher than the desired level of performance, which indicates high bias. Additionally, the dev error will be much larger than the training error, which indicates high variance. The conclusion drawn from this pattern is that you will have to find a way to reduce both bias and variance in your algorithm.

Key points:

• Training error is much higher than desired performance.
• Dev error is much higher than training error.
• Algorithm changes must aim to reduce both bias and variance.

Rubric: A strong answer must clearly state the relationship between training error and desired performance (high bias), the relationship between dev error and training error (high variance), and the required action (reducing both).