Convergence in logistic regression ( https://github.com/scikit-learn/scikit-learn/issues/11536) is indeed one problem (and it presents a general issue of what max_iter means when you have several solvers, or how good defaults are selected). But I was sure we had problems with non-determinism on some platforms... but now can't find.
> my students have basically no way to figure out what features the coefficients in their linear model correspond to, that seems a bit more important to me. Yes, I agree... Assuming coefficients are helpful, rather than using permutation-based measures of importance, for instance. I generally think a review of distances might be a good thing at some point, given the confusing triplication across sklearn.neighbors, sklearn.metrics.pairwise, scipy.spatial... and that minkowski,p=2 is not implemented the same as euclidean. On Thu, 14 Feb 2019 at 12:56, Andreas Mueller <t3k...@gmail.com> wrote: > Do you have a reference for the logistic regression stability? Is it > convergence warnings? > > Happy to discuss the other two issues, though I feel they seem easier than > most of what's on my list. > > I have no idea what's going on with OPTICS tbh, and I'll leave it up to > you and the others to decide whether that's something we should discuss. > I can try to read up and weigh in but that might not be the most effective > way to do it. > > the sample props is something I left out because I personally don't feel > it's a priority compared to all the other things; > my students have basically no way to figure out what features the > coefficients in their linear model correspond to, that seems a bit more > important to me. > > We can put it on the discussion list again, but I'm not super enthusiastic > about it. > > How should we prioritize things? > > > On 2/13/19 8:08 PM, Joel Nothman wrote: > > Yes, I was thinking the same. I think there are some other core issues to > solve, such as: > > * euclidean_distances numerical issues > * commitment to ARM testing and debugging > * logistic regression stability > > We should also nut out OPTICS issues or remove it from 0.21. I'm still > keen on trying to work out sample props (supporting weighted scoring at > least), but perhaps I'm being persuaded this will never be a top-priority > requirement, and the solutions add much complexity. > > On Thu, 14 Feb 2019 at 07:39, Andreas Mueller <t3k...@gmail.com> wrote: > >> Hey all. >> >> Should we collect some discussion points for the sprint? >> >> There's an unusual amount of core-devs present and I think we should >> seize the opportunity. >> Maybe we should create a page in the wiki or add it to the sprint page? >> >> Things that are high on my list of priorities are: >> >> - slicing pipelines >> - add get_feature_names to pipelines >> - freezing estimator >> - faster multi-metric scoring >> - fit_transform doing something other than fit.transform >> - imbalance-learn interface / subsampling in pipelines >> - Specifying search spaces and valid hyper parameters ( >> https://github.com/scikit-learn/scikit-learn/issues/13031). >> - allowing EstimatorCV-style speed-up in GridSearches >> - storing pandas column names and using them as feature names >> >> >> Trying to discuss all of these might be too much, but maybe we can figure >> out a subset and make sure we have sleps to discuss? >> Most of these issues are on the roadmap, issue 13031 is reladed to #18 >> but not directly on the roadmap. >> >> Thanks, >> Andy >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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