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I've implemented Random Forest regression algorithm in R (randomForest package) and GEE, but they are giving me very different results (average difference is 4% o and going up to 19% in some cases), and mainly the extreme values (low and high) differ very much.

I'm controlling for: number of trees variables per split seed node size. And this is all the common parameters between the two implementations, as far as I understand.

What else can I do to improve the agreement between the two? I did not found any useful documentation of the GEE implementation.

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A couple things could be going on. Based on the nature of the algorithm the seed may not control the randomization. Also, GEE and R have a different default for the bag fraction, or how much data is withheld when growing the forest. I believe GEE is set to 0.5 and R is like 0.63 or so. Additionally, GEE can be set to run in out-of-bag mode or not.

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