What causes the test error? Going beyond bias-variance via ANOVA

Understand

Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level.

• This can seem puzzling; in the worst case, such models do not need to generalize.
• This puzzle inspired a great amount of work, arguing when overparametrization reduces test error, in a phenomenon called "double descent".
• Recent work aimed to understand in greater depth why overparametrization is helpful for generalization.