5 ms·
This seems like a guaranteed recipe for overfitting. You’re searching over an enormous space of analytic functions and reporting fits without (as far as I can t
by goodside 7y ago
This seems like a guaranteed recipe for overfitting. You’re searching over an enormous space of analytic functions and reporting fits without (as far as I can tell) any regularization penalty on model complexity or validation on holdout data. Your model will fit well to any data you throw at it under those conditions, and the result is very unlikely to generalize.
I’m sorry if I’ve grossly misunderstood what you’re doing here, but trying to sell this method in a GUI tool (making it usable by people without a background in statistics) seems almost negligent.
- raverbashing 7y agoOverfitting is usually limited by the complexity of the reverted equation. It's usually a lesser problem in symbolic regression than in NNs But yeah, with such small sample size it might not generalize as much
- deckar01 7y agoThe previous submission on "Machine learning prediction of the coronavirus outbreak" indicates to me that they do not understand what problems symbolic regression can be applied to. They are making predictions about the future based only on past measurements of one event. They also seem to be revising their numbers without noting the changes in the article. https://web.archive.org/web/*/https://turingbotsoftware.com/posts/coronavirus-prediction.html https://web.archive.org/web/*/https://turingbotsoftware.com/...
- barcadad 7y agoCompletely agree - this is the textbook definition of overfitting - and recommending it for novices is like statistical malpractice. Why think it’s ok to dumb down data science so much? We don’t use “Be your own family’s surgeon” or “Represent your mom in court” apps! Expertise matters in ML/stats too...