I think this is a straightforward question, although the reasoning behind why or why not may not be. The reason I ask is that I have recently written my own implementation of a RF and although it performs well it is not performing quite as well as I had expected (based on the Kaggle Photo Quality Prediction competition data set, the winning scores and some of the subsequent info that came available about what techniques were used).
The first thing I do in such circumstances is plot prediction error for my model, so for each given prediction value I determine the mean bias (or deviation) away from the correct target value. For my RF I got this plot:
I'm wondering if this is a commonly observed bias pattern for RF (if not then it could perhaps be something specific to the data set and/or my implementation). I can of course use this plot to improve predictions by using it to compensate for the bias, but I'm wondering if there's a more fundamental error or shortcoming in the RF model itself that needs addressing. Thankyou.
== ADDENDUM ==
My initial investigation is at this blog entry Random Forest Bias - Update