I am currently writing a blog where I plan to do a regression model using Random Forest. Random Forest averages the estimated prediction from many decision trees that are fitted on to a bootstrap from the dataset while choosing
K<=M variables randomly from all the
M dependent variables.
However, I recently came across this presentation. One of the slides says something like this:
Here, the author is building a regression model using Random Forest with Residual Sum of Squares as the splitting criterion. During the splitting process, it is stated that a split is "too close to the edge."
My question is, what does an "edge" mean in this case? And why not choose a split if it is too close to the "edge"?
Thanks in advance.