The underlying principle of this technique is that several weak learners combined to provide a strong learner. The steps involved are

☛ Build several decision trees on bootstrapped training samples of data

☛ On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors

☛ Rule of thumb: At each split m=p√m=p

☛ Predictions: At the majority rule