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Very Happy to see FIGS on the list. I almost got a chance to use it on a recent project but the csutomer in the end decided they valued accuracy over interpreta
by frakt0x90 2y ago
Very Happy to see FIGS on the list. I almost got a chance to use it on a recent project but the csutomer in the end decided they valued accuracy over interpretability. I wonder if the recent KANs fit into this? They're more interpretable than other NN architectures. Also, Berkeley has had a few articles on interpretable methods (including FIGS) over the years but idk how they compare to what you've already implemented:
https://bair.berkeley.edu/blog/2022/02/02/imodels/ https://bair.berkeley.edu/blog/2022/02/02/imodels/
https://bair.berkeley.edu/blog/2020/04/23/decisions/ https://bair.berkeley.edu/blog/2020/04/23/decisions/
- 3abiton 2y agoQRD on FIGS for the curious ones?
- frakt0x90 2y agoThis explains it better than I could: https://bair.berkeley.edu/blog/2022/06/30/figs/ https://bair.berkeley.edu/blog/2022/06/30/figs/
- bravura 2y agoPretty cool. So it greedily constructs an ensemble of trees from scratch. At each step, it takes the one decision that reduces the loss the most. That's a nice approximation for the most compact tree ensemble.
- abhgh 2y agoI have used a successor [1] of FIGS and my experience was that although the theory is elegant, their benchmarking was incomplete.This is something I would look out for if you are planning to use it on a real-world problem. Again, this is not to be negative about the paper, but there is a gap between the theory and practice. I had raised this as an issue [2] on their repo, but after a few exchanges didn't receive any response. If you look at the last comment on that issue thread, you will see that Random Forest with proper hyperparam. search produces competitive results. [1] https://proceedings.mlr.press/v162/agarwal22b.html https://proceedings.mlr.press/v162/agarwal22b.html [2] https://github.com/csinva/imodels/issues/129 https://github.com/csinva/imodels/issues/129