I will try to draw a little context to my question from title.
I build a Random Forest Regressor from 1000 trees using sklearn. Then I exported all the decision paths along with predicted values for each Path and tree (around 50k decision paths, as one tree includes many decision paths).
I want to predict value for a particular sample. I can't use sklearn method predict() for some reasons but I have its value for comparison. I expected that I will take all decision paths for which the data from my sample meet conditions and calculate average to get final predicted value.
For example, if 3 decision paths corresponds to values from my sample, I could calculate average from 3 predicted values to get the final one.
But result from this operation differs a lot from value obtained by predict() method.
I would be grateful if someone could explain how this regressor works. And the second question is, is there any other way to obtain predicted value based only on decision paths of trees in the regressor.
Thank you very much!
And sorry for not attaching any code. I can clarify the description more if needed :)