What is the theoretical decomposition of Random Forest generalization error?

Error decomposes into bias, variance, and irreducible noise components.

Random Forest reduces variance by averaging correlated base learners. Bias remains similar to individual trees. The total expected error can be decomposed as E[(Y - f̂(X))²] = Bias² + Variance + Noise. RF primarily targets the variance term while leaving bias largely unchanged.