Random Forests are a widely used Machine Learning technique for both regression and classification. In this video, we show you how decision trees can be ensembled to create powerful predictive models.

Note: Some libraries randomize features at the split level rather than the tree level. This does not matter if we assume the trees are decision stumps, meaning there is only 1 split, or max depth = 1. In both cases, the goal is to limit the number of possible features in order to decorrelate the individual trees.