Solved – Random forests for multivariate regression

Question

I have a multi-output regression problem with $d_x$ input features and $d_y$ outputs. The outputs have a complex, non-linear correlation structure.

I'd like to use random forests to do the regression. As far as I can tell, random forests for regression only work with a single output, so I would have to train $d_y$ random forests – one for each output. This ignores their correlations.

Is there an extension to random forests that takes output correlations into account? Maybe something like Gaussian process regression for multi-task learning.

Answer 1

Here's an example of a multi-output regression problem undertaken with facial recognition. It includes a coding sample as well, it should give you a start with your methodology. http://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html