Re: [Scikit-learn-general] Classifier that is perfectly stable given shuffled training data

Hi everyone, Yeah, I'm aware that floating point operations cause problems, but some numerically stable algorithms do better than naive approaches at preventing large deviations. I don't know how much better though. Been a while since I took numerical methods. =) Re: data, sure, it's in

Re: [Scikit-learn-general] Classifier that is perfectly stable given shuffled training data

Hi Juan. For up to floating point precision, that is pretty hard as Gael mentioned. 1e-5 on sigma seems pretty low, though. Can you post data to reproduce? I would expect most classifiers to go to around 1e-8. Cheers, Andreas On 02/02/2015 10:46 AM, Juan Nunez-Iglesias wrote: Hi all, *TL;DR

Re: [Scikit-learn-general] Classifier that is perfectly stable given shuffled training data

Thinking about it, that's going to be hard: even floating points operations such as a sum of many floating point numbers is not permutation invariant, due to the rounding errors. Gaël -- Dive into the World of Parallel Pr