Ok so I think I have listened to a few wrong discussions on random forests because now I have a very confused question. With respect to Random Forests and bagging/bootstrapping, I'm good there. The confusing part is how the random selection of attributes is sometimes called "boosting". Basically from what I know of boosting, it is making many weak learners strong learners, so there would be only a few attributes in a boosting tree and it would be combined with many other like trees to have a strong learner. So is this applied to random forests too? And if it is not, then how can the attribute selection of random forrests be said to be different than boosting.
They are both ensemble methods. But rf treats each tree equally. You can train multiple trees in parallel. And the final result is just the average. But in boosting trees, trees are trained sequentially, that is new tree is trained on the residuals left by former predictor. In general, boosting has better performance but is easier to overfit and takes more time to train.