ensemble learning: compare multiple classifer models and make better decision to reduce the prediction error 1. Independent ensemble learning: ml models operate in parallel -> fast1) voting: train multiple type of models with train data and vote to classify itEx) plurality voting, majority voting- we can also mathematically prove that n classifiers with same error rate e can exhibit lower or sam..