# Random Forest for continuous response variable

I am running randomForest on a dataset which has a continuous dependent variable. Is there a way to get coefficients of the predictors as in linear regression?

• You can visualize your RF model structure with forestFloor and check how non-linear your fit is and what interactions there are. By learning structure you may be able to create a ridge regression model possible with some transformations of variables and interaction terms. – Soren Havelund Welling Sep 23 '15 at 5:58

$$E(y \mid x) = \beta \cdot x + \epsilon$$
and find the coefficients $\beta$ that best fits this postulated relationship to your observed data.
In the absence of such a structured postulated form of the $y$ to $x$ relationship, no terse yet complete summary of the model is possible.
Generally, models that can be completely summarized with a finite, small vector in $R^n$ are referred to as parametric, and those that cannot are dubbed non-parametric. Random forest is a non-parametric model.