How does bias-variance tradeoff manifest in deep neural networks compared to classical ML models?

Deep networks can achieve low bias and low variance simultaneously with enough data, unlike classical models where tradeoff is stricter.

In classical ML, the bias-variance tradeoff is rigid: reducing bias increases variance. However, deep learning behaves differently in high-data regimes. Over-parameterized neural networks can generalize well despite high complexity due to implicit regularization from optimization methods like SGD.

Modern theory suggests double descent behavior, where test error decreases, increases, then decreases again as model complexity grows. This challenges traditional bias-variance assumptions and shows deep learning can sometimes bypass the classical tradeoff.