How does feature interaction modeling affect bias and variance in large-scale systems?

Modeling feature interactions reduces bias by capturing complex relationships but increases variance due to higher model complexity.

Feature interactions capture dependencies between variables that are not visible individually. Linear models ignore these interactions, leading to high bias. Techniques like polynomial features, factorization machines, and deep neural networks explicitly model interactions.

However, increasing interaction complexity expands the hypothesis space, making the model more sensitive to noise, which increases variance. In large-scale systems, controlling interaction depth is critical to avoid overfitting.…