Machine Learning, Uc Berkeley, Random Forest, Decision Tree, Ensemble Learning.
m) of the pp variables as possible candidate variables to split upon. At each new split point, we randomly choose m<p variables to be in our subset of candidates, and consider only those m variables. So, for our first decision tree’s first split point, let’s only consider location, square footage,