What is the bias-variance tradeoff interpretation of Naïve Bayes?

Naïve Bayes has high bias due to independence assumptions but very low variance.

The independence assumption simplifies the model, introducing structural bias. However, because NB estimates only simple frequency-based parameters, it is stable across different training samples, resulting in low variance. This makes NB effective in small-data regimes where high-variance models like decision trees overfit.