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 Thanks for considering contributing! Please read this document to learn the various ways you can contribute to this project and how to go about doing it.
 
 **Submodule dependency on a fork of scikit-learn**
-Due to the current state of scikit-learn's internal Cython code for trees, we have to instead leverage a maintained fork of scikit-learn at <https://github.com/neurodata/scikit-learn>, where specifically, the `submodulev2` branch is used to build and install this repo. We keep that fork well-maintained and up-to-date with respect to the main sklearn repo. The only difference is the refactoring of the `tree/` submodule. This fork is used internally under the namespace ``sktree._lib.sklearn``. It is necessary to use this fork for anything related to:
+Due to the current state of scikit-learn's internal Cython code for trees, we have to instead leverage a maintained fork of scikit-learn at <https://github.com/neurodata/scikit-learn>, where specifically, the `submodulev3` branch is used to build and install this repo. We keep that fork well-maintained and up-to-date with respect to the main sklearn repo. The only difference is the refactoring of the `tree/` submodule. This fork is used internally under the namespace ``sktree._lib.sklearn``. It is necessary to use this fork for anything related to:
 
 - `RandomForest*`
 - `ExtraTrees*`
 - or any importable items from the `tree/` submodule, whether it is a Cython or Python object
 
-If you are developing for scikit-tree, we will always depend on the most up-to-date commit of `https://github.com/neurodata/scikit-learn/submodulev2` as a submodule within scikit-tee. This branch is consistently maintained for changes upstream that occur in the scikit-learn tree submodule. This ensures that our fork maintains consistency and robustness due to bug fixes and improvements upstream
+If you are developing for scikit-tree, we will always depend on the most up-to-date commit of `https://github.com/neurodata/scikit-learn/submodulev3` as a submodule within scikit-tee. This branch is consistently maintained for changes upstream that occur in the scikit-learn tree submodule. This ensures that our fork maintains consistency and robustness due to bug fixes and improvements upstream
 
 ## Bug reports and feature requests