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How Do You Work Towards a Random Forest?

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The underlying principle of this technique is that several weak learners combined to provide a keen 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=pvm=p

Predictions: At the majority rule

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