I just wrote a python program to convert decision trees into neural networks.
The idea is pretty much described in this paper: https://www.cise.ufl.edu/~arunava/papers/clnl94.pdf
It was really fun and I hope to put up the code onto github once I do some analysis of how effective (or ineffective) it is.
My hope is to apply this to a random forest and then train each resultant net in order to get better performance than random forests.
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Tomorrow I'm going to do some analysis of how effective it is, but this isn't quite my top priority right now, since I'm taking the GRE on Tuesday
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