Averaging an ensemble's predictions preserves shared signal and cancels uncorrelated errors

It’s based on the premise that there’s a single truth among all the trees (correlated, so the signal is shared across the trees), but due to the randomness of ensembles (ex. trees generated randomly based on a subset of rows and features), the errors are not correlated between trees, so they naturally get smoothened out.