How To Work Towards A Random Forest?

1.	How To Work Towards A Random Forest?
Answer» UNDERLYING principle of this TECHNIQUE is that SEVERAL weak LEARNERS combined 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. Underlying principle of this technique is that several weak learners combined provide a strong learner. The steps involved are

Answer»

UNDERLYING principle of this TECHNIQUE is that SEVERAL weak LEARNERS combined 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.

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

Discussion