Python: Symbolic Regression

[@nothingness1983]

Thanks for the video. Really very useful for Physicists.

[@johannescartus9847]

Very interesting video, but now I have some questions 🤔 In practice it is rarely the case that one has data that perfectly adheres to an analytic relationship of the variables. E.g., the measured data might be noisy and/or the underlying problem does not have a closed form solution. How well does this method perform if you, e.g., we’re to add some noise to your example data? Also, since it will always find some result, is there any way to tell when it finds something that is actually describing the underlying problem (as opposed to just finding a random formula that happens to fit the noise)?

[@SyedMehmud]

Good question. I think in many situations it will fail to form a closed form solution, which makes this a limited application algorithm. Still can be fun/useful to try it on data and see if it gives any insight, even if imperfect. With a little noise and strong underlying signal, it can discover the signal but at some point the noise will overwhelm it. I haven't experimented much along these lines however.

[@enlightenment609]

Genetic Programming based symbolic regression suffers from noise like other ML methods do. Therefore approaches to mitigate noise in other methods can also work for GP. For example you can use chi square error, which normalises the error term with the standard deviation of noise, instead of simple mean squared error. The benefit of GP is in its symbolic solutions but if the solution is very large, then interpreting it is non trivial. Therefore, discouraging complexity while maximising accuracy is the challenge.

[@rahulbpillai22]

thank you for the video. Can you make a video on Mutigene Genetic Programming for regression problem in python

[@santoshkhanal7982]

Really nice video. Could you please make a video using symbolic regression on real-world data such as AutoMPG or California House Price or abalone dataset ( small dataset) or something like that? Thank you!

[@sammydemmi448]

Check out this intro to the QLattice a new symbolic regressor applied to a heart failure problem m.kzbin.info/www/bejne/e5K6f4ucrtGXY9k

[@johnchen2022]

very interesting, thx for the video

[@FreeMarketSwine]

Can this be used for optimization or reinforcement learning?

[@zhuoxuanli2277]

When I use SymbolicRegression to fit my data, the final formula is always a constant. I don't know why :(

[@ahmed-pk6gy]

Hello, how may I contact you sir?

[@DistortedV12]

I'm waiting for the Neurips paper that says: Symbolic Regression is solved, P != NP

[@priyamgupta8170]

i am mad do you all know