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Treebomination: Convert a scikit-learn decision tree into a Keras model
- opensandwich 3y agoJust an FYI - you can achieve this in 3 layers. It does not need to be deep. https://github.com/charliec443/TreeGrad https://github.com/charliec443/TreeGrad
- Dobiasd 3y agoYeah, this one does something much less insane, i.e., it converts the paths to the tree outputs into their corresponding DNS (disjunctive normal form) and represents each term as a node (side by side in the same layer) in the NN, as described by Arunava Banerjee in "Initializing Neural Networks using Decision" [1]. The resulting NN architecture is much more reasonable than the one that treebomination produces. [1]: https://www.cise.ufl.edu/~arunava/papers/clnl94.pdf https://www.cise.ufl.edu/~arunava/papers/clnl94.pdf
- syntaxing 3y agoYou can also use Tensorflow decision forest to begin with [1]. [1] https://www.tensorflow.org/decision_forests https://www.tensorflow.org/decision_forests
- Dobiasd 3y agoThank! Yes, in contrast to treebomination, using TF-DF can actually make sense. ;)
- thangngoc89 3y agoSee also hummingbird [1] [1]: https://github.com/microsoft/hummingbird https://github.com/microsoft/hummingbird
- Dobiasd 3y agoThanks! This looks interesting. Some of the main differences I can spot so far are: - Hummingbird does not construct a NN with an architecture isomorphic to the source decision tree but instead cleverly compiled it into other (more sane) tensor computations. - Hummingbird is actually useful. ;)
- StackOverlord 3y ago[flagged]