How do I get coefficients of a random forest model? I am using randomForest to generate a model, and at the end I don't know how I can get the final coefficients that the model is fitting. I know that for linear regression, you just type summary(lm) where lm is your model, and you get the coefficients.
 A: @Community brought this question from the past. Unfortunately the both answers from 2014 do not answer the question. The answer to the question is in @StephanKolassa comment: there are is no meaningful concept of coefficient for RF (except the importance measure of RF).
I also must add that I found @MichaelMayer comment that "random forests are not very useful in combining strong learners such as regression trees" very surprising. In my very limited experience with regression, RF is always a competitive algorithm (together with MLP) - I have not yet used GBM or Gaussian Processes for regression.
A: You can estimate coefficients one at a time. This makes the most sense if you are approaching the problem from a causal inference perspective.
The idea is to estimate y ~ x to predict hat{y}. Then estimate in a linear model, (y - hat{y}) ~ z. This is analogous to E[E[y|x]|z] = E[y|z,x], which will yield an estimate of the effect of z on y given x, under some standard selection assumption. 
In theory you could do this piecewise regression throughout, but note your SEs will be incorrect (and possibly meaningless since it's not clear what sampling theory is motivated them). Anyway these should probably should be bootrapped, assuming bootstrapping random forests makes sense...
