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yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
There seems to be differentiable tree models now that perfor somewhat better than e.g. XGBoost https://github.com/Evovest/MLBenchmarks.jl?tab=readme-ov-fil...
Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good.
For example, take the circle dataset here: https://playground.tensorflow.org
That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by radius would do the trick. Now try doing that in 1000 dimensions. Sometimes you can, sometimes you can't or do want to bother.
Is it this library https://github.com/google/yggdrasil-decision-forests ?
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