Solved – How to implement a customized Random Forest classifier

I am trying to replicate an extension of Random Forests introduced in a recent research publication for my project.
For instance, for the binary split of data at each node, instead of randomly assigning all example from each class a binary label, they suggest using an SVM to learn a binary split of data. Furthermore, features at a node are augmented with the decision value of its parent node.

I wanted to know, if there exists an implementation of Random Forest which can be built on?

I think you need an open-source, readable version of random forests to be able to digg into the code and propose your modifications.

All the basic building blocks you need are available in scikit.learn if you know some python – plus the community is very active and proved to be able to help such efforts in the past.

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