Question

a learning algorithm with low bias and high variance may be suitable under what circumstances?

Answer 1

Explanation:

✍In supervised learning, underfitting happens when a model unable to capture the underlying pattern of the data.

These models usually have high bias and low variance.

These models have low bias and high variance. These models are very complex like Decision trees which are prone to overfitting.

Answer 2

If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it's going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data.