> 1) Random forests are by nature supervised learners You can run unsupervised random forests. The way this procedure works is to first make a "copy" of the or

> 1) Random forests are by nature supervised learners You can run unsupervised random forests. The way this procedure works is to first make a "copy" of the original dataset (the reason for the quotes will be evident in a second), and then train the forest to distinguish the original data from the copy with a binary target vector. The "copy" is made by sampling column-wise from the original dataset, so that, in the copy, each variable has the same univariate distribution as the original, but any interdependencies are destroyed. The forest then learns to distinguish the original from the copy, and the only information that it has at its disposal are interdependencies in the original dataset.