What is the effect of PCA on bias-variance tradeoff?

PCA typically reduces variance at the cost of increased bias.

By reducing dimensionality, PCA removes noise and reduces model complexity, lowering variance. However, it may discard useful information, increasing bias. The net effect often improves generalization when data is noisy or high-dimensional.