Tell us how do you work towards a random forest?
Submitted by: MuhammadThe 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
Submitted by: Muhammad
☛ 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
Submitted by: Muhammad
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