<div dir="ltr"><div>I am really interested in the union of the list given by Andy and Joel.</div><div><br></div><div>I'll like to have some discussions related to the "impute" module. Compare to the other topics, it is not a high priority discussion thought.<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, 14 Feb 2019 at 05:31, Joel Nothman <<a href="mailto:joel.nothman@gmail.com">joel.nothman@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Convergence in logistic regression (<a href="https://github.com/scikit-learn/scikit-learn/issues/11536" target="_blank">https://github.com/scikit-learn/scikit-learn/issues/11536</a>) 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.<br></div><div dir="ltr"><br></div><div dir="ltr">> 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.</div><div dir="ltr"><br></div><div>Yes, I agree... Assuming coefficients are helpful, rather than using permutation-based measures of importance, for instance.</div><div><br></div><div><div>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.</div><br class="gmail-m_115413914047657351gmail-Apple-interchange-newline"></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, 14 Feb 2019 at 12:56, Andreas Mueller <<a href="mailto:t3kcit@gmail.com" target="_blank">t3kcit@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div bgcolor="#FFFFFF">
<p>Do you have a reference for the logistic regression stability? Is
it convergence warnings?</p>
<p>Happy to discuss the other two issues, though I feel they seem
easier than most of what's on my list.</p>
<p>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.<br>
I can try to read up and weigh in but that might not be the most
effective way to do it.</p>
<p>the sample props is something I left out because I personally
don't feel it's a priority compared to all the other things;<br>
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.</p>
<p>We can put it on the discussion list again, but I'm not super
enthusiastic about it.</p>
<p>How should we prioritize things?</p>
<p><br>
</p>
<div class="gmail-m_115413914047657351gmail-m_-4195171471883286738moz-cite-prefix">On 2/13/19 8:08 PM, Joel Nothman wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">Yes, I was thinking the same. I think there are
some other core issues to solve, such as:
<div><br>
<div>* euclidean_distances numerical issues</div>
<div>* commitment to ARM testing and debugging</div>
<div>* logistic regression stability</div>
<div><br>
</div>
<div>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.</div>
</div>
</div>
<br>
<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Thu, 14 Feb 2019 at 07:39,
Andreas Mueller <<a href="mailto:t3kcit@gmail.com" target="_blank">t3kcit@gmail.com</a>> wrote:<br>
</div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div bgcolor="#FFFFFF"> Hey all.<br>
<br>
Should we collect some discussion points for the sprint?<br>
<br>
There's an unusual amount of core-devs present and I think
we should seize the opportunity.<br>
Maybe we should create a page in the wiki or add it to the
sprint page?<br>
<br>
Things that are high on my list of priorities are:<br>
<ul>
<li>slicing pipelines</li>
<li>add get_feature_names to pipelines</li>
<li>freezing estimator</li>
<li>faster multi-metric scoring</li>
<li>fit_transform doing something other than fit.transform</li>
<li>imbalance-learn interface / subsampling in pipelines</li>
<li>Specifying search spaces and valid hyper parameters (<a class="gmail-m_115413914047657351gmail-m_-4195171471883286738gmail-m_1283089229977627558moz-txt-link-freetext" href="https://github.com/scikit-learn/scikit-learn/issues/13031" target="_blank">https://github.com/scikit-learn/scikit-learn/issues/13031</a>).</li>
<li>allowing EstimatorCV-style speed-up in GridSearches</li>
<li>storing pandas column names and using them as feature
names</li>
</ul>
<br>
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?<br>
Most of these issues are on the roadmap, issue 13031 is
reladed to #18 but not directly on the roadmap.<br>
<br>
Thanks,<br>
Andy<br>
</div>
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</blockquote></div><br clear="all"><br>-- <br><div dir="ltr" class="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div>Guillaume Lemaitre<br>INRIA Saclay - Parietal team<br>Center for Data Science Paris-Saclay<br><a href="https://glemaitre.github.io/" target="_blank">https://glemaitre.github.io/</a></div></div></div></div></div></div></div>