Recall the bias-variance decomposition of classification error and how bagging affects each component.

Derive the upper bound on the ensemble error in terms of the individual classifier error and the correlation between classifiers.

Prove that the ensemble error decreases as the number of base classifiers increases, assuming their errors are independent and identically distributed.