Hello,
Is there an accepted way to convey, for regression trees, something
akin to R-squared?
I'm developing regression trees for a continuous y variable and I'd
like to say how well they are doing. In particular, I'm analyzing the
results of a simulation model having highly non-linear behavior, and
asking what characteristics of the inputs are related to a particular
output measure. I've got a very large number of points: n=4000. I'm
not able to do a model sensitivity analysis because of the large
number of inputs and the model run time.
I've been googling around both on the archives and on the rest of the
web for several hours, but I'm still having trouble getting a firm
sense of the state of the art. Could someone help me to quickly
understand what strategy, if any, is acceptable to say something like
"The regression tree in Figure 3 captures 42% of the variance"? The
target audience is readers who will be interested in the subsequent
verbal explanation of the relationship, but only once they are
comfortable that the tree really does capture something. I've run
across methods to say how well a tree does relative to a set of trees
on the same data, but that doesn't help much unless I'm sure the
trees in question are really capturing the essence of the system.
I'm happy to be pointed to a web site or to a thread I may have
missed that answers this exact question.
Thanks very much,
Jeff
------------------------------------------
Prof. Jeffrey Cardille
[EMAIL PROTECTED]
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