I have a dataset including discrete and continuous variables on which I ran a random forest model using the r-package randomForest. I have read several times that the output of a RF model is a "black box".
I know that I can actually get the splitting points of the numerical values by tree. However, I would like to know, if I could extract a "representative" splitting point which I could use, in general (not specific to only 1tree).
Not really. All splits after the first are conditional on the previous splits, so even if you only look at splits on some variable, it doesn't have the same meaning -- it's the difference between $X|Y$ and $X|Z$ etc. Imagine that you're classifying points in a circle like in this example. If you're examining $x_1$, sometimes you split near 1 and sometimes near -1, but it's going to be hard to make sense of that out of context. @gung's suggestion to look at this thread is a good one.
