On 2/13/19 11:28 PM, Joel Nothman wrote:
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.
You would apply the permutation based feature importances before any
preprocessing? I guess there's a case to be made for either option.
I think there are good reasons to look at coefficients though.
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.
Yes, I agree. I guess right now I'm more enthusiastic about new
features/APIs than decreasing technical debt, maybe because you're the
one dealing with the technical debt ;)
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