Treebomination: Convert a scikit-learn decision tree into a Keras model
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Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#2Just an FYI - you can achieve this in 3 layers. It does not need to be deep.
https://github.com/charliec443/TreeGrad
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#3You can also use Tensorflow decision forest to begin with [1].
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#4See also hummingbird [1]
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#5See also hummingbird [1] [1]: https://github.com/microsoft/hummingbird
Thanks! 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. ;)
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#6You can also use Tensorflow decision forest to begin with [1]. [1] https://www.tensorflow.org/decision_forests
Thank! Yes, in contrast to treebomination, using TF-DF can actually make sense. ;)
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
#7Just an FYI - you can achieve this in 3 layers. It does not need to be deep. https://github.com/charliec443/TreeGrad
Yeah, 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.
Re: Treebomination: Convert a scikit-learn decision tree into a Keras model
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