<div dir="ltr"><div>Dear Scikit-learn,</div><div><br></div><div>This is my first message in this community!</div><div><br></div><div>I make it because I think "model complexity" and "model prediction" are two separate "properties", which cannot in principle be directly compared. This is because one variable is missing, which is the data.</div><div>If the initial data set corresponds to the entire true range of possible data, then I would say complex models will "model"
the variable being studied
with a prediction accuracy equal or better
than any other "less complex" model. If the data set is not representative, then you might overfit with more complex models and there is a chance that more simple models will predict better for unseen sets of data.</div><div>Therefore, the quality of the data is critical to judge how good will your model be.</div><div>Hope this helps.</div><div>João</div><div><br></div><div><br></div><div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div>João André<br>Civil Engineer, M.Sc., Ph.D.<br>Structures Department<br>National Laboratory for Civil Engineering<br>LNEC, Av. Brasil 101, 1700-066 Lisbon, Portugal<br>Web: <a href="http://www.lnec.pt/" target="_blank">http://www.lnec.pt/</a><br>Skype ID: jpcgandre<br>Phone: (+351) 218 443 355</div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, 16 Oct 2019 at 15:05, Gael Varoquaux <<a href="mailto:gael.varoquaux@normalesup.org">gael.varoquaux@normalesup.org</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">On Sun, Oct 13, 2019 at 07:40:11PM +0900, Brown J.B. via scikit-learn wrote:<br>
> Please, respect and refinement when addressing the contributors and users of<br>
> scikit-learn.<br>
<br>
I believe that Mike simply misread. It's something that happens (it<br>
happens a lot to me).<br>
<br>
No harm on my side, and thanks for clarifying my overly short reply.<br>
<br>
G<br>
<br>
> Gael's statement is perfect -- complexity does not imply better prediction.<br>
> The choice of estimator (and algorithm) depends on the structure of the model<br>
> desired for the data presented.<br>
> Estimator superiority cannot be proven in a context- and/or data-agnostic<br>
> fashion.<br>
<br>
> J.B.<br>
<br>
<br>
> 2019年10月13日(日) 6:13 Mike Smith <<a href="mailto:javaeurusd@gmail.com" target="_blank">javaeurusd@gmail.com</a>>:<br>
<br>
> "Second complexity does not<br>
> > imply better prediction. " <br>
<br>
> Complexity doesn't imply prediction? Perhaps you're having a translation<br>
> error.<br>
<br>
> On Sat, Oct 12, 2019 at 2:04 PM <<a href="mailto:scikit-learn-request@python.org" target="_blank">scikit-learn-request@python.org</a>> wrote:<br>
<br>
> Send scikit-learn mailing list submissions to<br>
> <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
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> To subscribe or unsubscribe via the World Wide Web, visit<br>
> <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
> or, via email, send a message with subject or body 'help' to<br>
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> When replying, please edit your Subject line so it is more specific<br>
> than "Re: Contents of scikit-learn digest..."<br>
<br>
<br>
> Today's Topics:<br>
<br>
> 1. Re: scikit-learn Digest, Vol 43, Issue 24 (Mike Smith)<br>
<br>
<br>
> ----------------------------------------------------------------------<br>
<br>
> Message: 1<br>
> Date: Sat, 12 Oct 2019 14:04:12 -0700<br>
> From: Mike Smith <<a href="mailto:javaeurusd@gmail.com" target="_blank">javaeurusd@gmail.com</a>><br>
> To: <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
> Subject: Re: [scikit-learn] scikit-learn Digest, Vol 43, Issue 24<br>
> Message-ID:<br>
> <CAEWZffD-hNviFkyxuM8CgDR3XSWOyn=<br>
> <a href="mailto:4LRy2NJvjwvVr4RgobQ@mail.gmail.com" target="_blank">4LRy2NJvjwvVr4RgobQ@mail.gmail.com</a>><br>
> Content-Type: text/plain; charset="utf-8"<br>
<br>
> "... > If I should expect good results on a pc, scikit says that<br>
> needing<br>
> gpu power is<br>
> > obsolete, since certain scikit models perform better (than ml<br>
> designed<br>
> for gpu)<br>
> > that are not designed for gpu, for that reason. Is this true?"<br>
<br>
> Where do you see this written? I think that you are looking for overly<br>
> simple stories that you are not true."<br>
<br>
> Gael, see the below from the scikit-learn FAQ. You can also find this<br>
> yourself at the main FAQ:<br>
<br>
> [image: 2019-10-12 14_00_05-Frequently Asked Questions ? scikit-learn<br>
> 0.21.3 documentation.png]<br>
<br>
<br>
> On Sat, Oct 12, 2019 at 9:03 AM <<a href="mailto:scikit-learn-request@python.org" target="_blank">scikit-learn-request@python.org</a>><br>
> wrote:<br>
<br>
> > Send scikit-learn mailing list submissions to<br>
> > <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
<br>
> > To subscribe or unsubscribe via the World Wide Web, visit<br>
> > <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
> > or, via email, send a message with subject or body 'help' to<br>
> > <a href="mailto:scikit-learn-request@python.org" target="_blank">scikit-learn-request@python.org</a><br>
<br>
> > You can reach the person managing the list at<br>
> > <a href="mailto:scikit-learn-owner@python.org" target="_blank">scikit-learn-owner@python.org</a><br>
<br>
> > When replying, please edit your Subject line so it is more specific<br>
> > than "Re: Contents of scikit-learn digest..."<br>
<br>
<br>
> > Today's Topics:<br>
<br>
> > 1. Re: Is scikit-learn implying neural nets are the best<br>
> > regressor? (Gael Varoquaux)<br>
<br>
<br>
<br>
> ----------------------------------------------------------------------<br>
<br>
> > Message: 1<br>
> > Date: Fri, 11 Oct 2019 13:34:33 -0400<br>
> > From: Gael Varoquaux <<a href="mailto:gael.varoquaux@normalesup.org" target="_blank">gael.varoquaux@normalesup.org</a>><br>
> > To: Scikit-learn mailing list <<a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a>><br>
> > Subject: Re: [scikit-learn] Is scikit-learn implying neural nets are<br>
> > the best regressor?<br>
> > Message-ID: <<a href="mailto:20191011173433.bbywiqnwjjpvsi4r@phare.normalesup.org" target="_blank">20191011173433.bbywiqnwjjpvsi4r@phare.normalesup.org</a>><br>
> > Content-Type: text/plain; charset=iso-8859-1<br>
<br>
> > On Fri, Oct 11, 2019 at 10:10:32AM -0700, Mike Smith wrote:<br>
> > > In other words, according to that arrangement, is scikit-learn<br>
> implying<br>
> > that<br>
> > > section 1.17 is the best regressor out of the listed, 1.1 to 1.17?<br>
<br>
> > No.<br>
<br>
> > First they are not ordered in order of complexity (Naive Bayes is<br>
> > arguably simpler than Gaussian Processes). Second complexity does not<br>
> > imply better prediction.<br>
<br>
> > > If I should expect good results on a pc, scikit says that needing<br>
> gpu<br>
> > power is<br>
> > > obsolete, since certain scikit models perform better (than ml<br>
> designed<br>
> > for gpu)<br>
> > > that are not designed for gpu, for that reason. Is this true?<br>
<br>
> > Where do you see this written? I think that you are looking for<br>
> overly<br>
> > simple stories that you are not true.<br>
<br>
> > > How much hardware is a practical expectation for running the best<br>
> > > scikit models and getting the best results?<br>
<br>
> > This is too vague a question for which there is no answer.<br>
<br>
> > Ga?l<br>
<br>
> > > On Fri, Oct 11, 2019 at 9:02 AM <<a href="mailto:scikit-learn-request@python.org" target="_blank">scikit-learn-request@python.org</a>><br>
> wrote:<br>
<br>
> > > Send scikit-learn mailing list submissions to<br>
> > > ? ? ? ? <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
<br>
> > > To subscribe or unsubscribe via the World Wide Web, visit<br>
> > > ? ? ? ? <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
> > > or, via email, send a message with subject or body 'help' to<br>
> > > ? ? ? ? <a href="mailto:scikit-learn-request@python.org" target="_blank">scikit-learn-request@python.org</a><br>
<br>
> > > You can reach the person managing the list at<br>
> > > ? ? ? ? <a href="mailto:scikit-learn-owner@python.org" target="_blank">scikit-learn-owner@python.org</a><br>
<br>
> > > When replying, please edit your Subject line so it is more<br>
> specific<br>
> > > than "Re: Contents of scikit-learn digest..."<br>
<br>
<br>
> > > Today's Topics:<br>
<br>
> > > ? ?1. Re: logistic regression results are not stable between<br>
> > > ? ? ? solvers (Andreas Mueller)<br>
<br>
<br>
<br>
> > <br>
> ----------------------------------------------------------------------<br>
<br>
> > > Message: 1<br>
> > > Date: Fri, 11 Oct 2019 15:42:58 +0200<br>
> > > From: Andreas Mueller <<a href="mailto:t3kcit@gmail.com" target="_blank">t3kcit@gmail.com</a>><br>
> > > To: <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
> > > Subject: Re: [scikit-learn] logistic regression results are not<br>
> > stable<br>
> > > ? ? ? ? between solvers<br>
> > > Message-ID: <<a href="mailto:d55949d6-3355-f892-f6b3-030edf1c7947@gmail.com" target="_blank">d55949d6-3355-f892-f6b3-030edf1c7947@gmail.com</a>><br>
> > > Content-Type: text/plain; charset="utf-8"; Format="flowed"<br>
<br>
<br>
<br>
> > > On 10/10/19 1:14 PM, Beno?t Presles wrote:<br>
<br>
> > > > Thanks for your answers.<br>
<br>
> > > > On my real data, I do not have so many samples. I have a bit<br>
> more<br>
> > than<br>
> > > > 200 samples in total and I also would like to get some<br>
> results with<br>
> > > > unpenalized logisitic regression.<br>
> > > > What do you suggest? Should I switch to the lbfgs solver?<br>
> > > Yes.<br>
> > > > Am I sure that with this solver I will not have any<br>
> convergence<br>
> > issue<br>
> > > > and always get the good result? Indeed, I did not get any<br>
> > convergence<br>
> > > > warning with saga, so I thought everything was fine. I<br>
> noticed some<br>
> > > > issues only when I decided to test several solvers. Without<br>
> > comparing<br>
> > > > the results across solvers, how to be sure that the<br>
> optimisation<br>
> > goes<br>
> > > > well? Shouldn't scikit-learn warn the user somehow if it is<br>
> not<br>
> > the case?<br>
> > > We should attempt to warn in the SAGA solver if it doesn't<br>
> converge.<br>
> > > That it doesn't raise a convergence warning should probably be<br>
> > > considered a bug.<br>
> > > It uses the maximum weight change as a stopping criterion right<br>
> now.<br>
> > > We could probably compute the dual objective once in the end to<br>
> see<br>
> > if<br>
> > > we converged, right? Or is that not possible with SAGA? If not,<br>
> we<br>
> > might<br>
> > > want to caution that no convergence warning will be raised.<br>
<br>
<br>
> > > > At last, I was using saga because I also wanted to do some<br>
> feature<br>
> > > > selection by using l1 penalty which is not supported by<br>
> lbfgs...<br>
> > > You can use liblinear then.<br>
<br>
<br>
<br>
> > > > Best regards,<br>
> > > > Ben<br>
<br>
<br>
> > > > Le 09/10/2019 ? 23:39, Guillaume Lema?tre a ?crit?:<br>
> > > >> Ups I did not see the answer of Roman. Sorry about that. It<br>
> is<br>
> > coming<br>
> > > >> back to the same conclusion :)<br>
<br>
> > > >> On Wed, 9 Oct 2019 at 23:37, Guillaume Lema?tre<br>
> > > >> <<a href="mailto:g.lemaitre58@gmail.com" target="_blank">g.lemaitre58@gmail.com</a> <mailto:<a href="mailto:g.lemaitre58@gmail.com" target="_blank">g.lemaitre58@gmail.com</a>>><br>
> wrote:<br>
<br>
> > > >>? ? ?Uhm actually increasing to 10000 samples solve the<br>
> convergence<br>
> > > issue.<br>
> > > >>? ? ?SAGA is not designed to work with a so small sample size<br>
> most<br>
> > > >>? ? ?probably.<br>
<br>
> > > >>? ? ?On Wed, 9 Oct 2019 at 23:36, Guillaume Lema?tre<br>
> > > >>? ? ?<<a href="mailto:g.lemaitre58@gmail.com" target="_blank">g.lemaitre58@gmail.com</a> <mailto:<a href="mailto:g.lemaitre58@gmail.com" target="_blank">g.lemaitre58@gmail.com</a>>><br>
> > wrote:<br>
<br>
> > > >>? ? ? ? ?I slightly change the bench such that it uses<br>
> pipeline and<br>
> > > >>? ? ? ? ?plotted the coefficient:<br>
<br>
> > > >>? ? ? ? ?<a href="https://gist.github.com/glemaitre/" rel="noreferrer" target="_blank">https://gist.github.com/glemaitre/</a><br>
> > > 8fcc24bdfc7dc38ca0c09c56e26b9386<br>
<br>
> > > >>? ? ? ? ?I only see one of the 10 splits where SAGA is not<br>
> > converging,<br>
> > > >>? ? ? ? ?otherwise the coefficients<br>
> > > >>? ? ? ? ?look very close (I don't attach the figure here but<br>
> they<br>
> > can<br>
> > > >>? ? ? ? ?be plotted using the snippet).<br>
> > > >>? ? ? ? ?So apart from this second split, the other<br>
> differences<br>
> > seems<br>
> > > >>? ? ? ? ?to be numerical instability.<br>
<br>
> > > >>? ? ? ? ?Where I have some concern is regarding the<br>
> convergence<br>
> > rate<br>
> > > >>? ? ? ? ?of SAGA but I have no<br>
> > > >>? ? ? ? ?intuition to know if this is normal or not.<br>
<br>
> > > >>? ? ? ? ?On Wed, 9 Oct 2019 at 23:22, Roman Yurchak<br>
> > > >>? ? ? ? ?<<a href="mailto:rth.yurchak@gmail.com" target="_blank">rth.yurchak@gmail.com</a> <mailto:<a href="mailto:rth.yurchak@gmail.com" target="_blank">rth.yurchak@gmail.com</a><br>
<br>
> > wrote:<br>
<br>
> > > >>? ? ? ? ? ? ?Ben,<br>
<br>
> > > >>? ? ? ? ? ? ?I can confirm your results with penalty='none'<br>
> and<br>
> > C=1e9.<br>
> > > >>? ? ? ? ? ? ?In both cases,<br>
> > > >>? ? ? ? ? ? ?you are running a mostly unpenalized logisitic<br>
> > > >>? ? ? ? ? ? ?regression. Usually<br>
> > > >>? ? ? ? ? ? ?that's less numerically stable than with a small<br>
> > > >>? ? ? ? ? ? ?regularization,<br>
> > > >>? ? ? ? ? ? ?depending on the data collinearity.<br>
<br>
> > > >>? ? ? ? ? ? ?Running that same code with<br>
> > > >>? ? ? ? ? ? ?? - larger penalty ( smaller C values)<br>
> > > >>? ? ? ? ? ? ?? - or larger number of samples<br>
> > > >>? ? ? ? ? ? ?? yields for me the same coefficients (up to<br>
> some<br>
> > > tolerance).<br>
<br>
> > > >>? ? ? ? ? ? ?You can also see that SAGA convergence is not<br>
> good by<br>
> > the<br>
> > > >>? ? ? ? ? ? ?fact that it<br>
> > > >>? ? ? ? ? ? ?needs 196000 epochs/iterations to converge.<br>
<br>
> > > >>? ? ? ? ? ? ?Actually, I have often seen convergence issues<br>
> with<br>
> > SAG<br>
> > > >>? ? ? ? ? ? ?on small<br>
> > > >>? ? ? ? ? ? ?datasets (in unit tests), not fully sure why.<br>
<br>
> > > >>? ? ? ? ? ? ?--<br>
> > > >>? ? ? ? ? ? ?Roman<br>
<br>
> > > >>? ? ? ? ? ? ?On 09/10/2019 22:10, serafim loukas wrote:<br>
> > > >>? ? ? ? ? ? ?> The predictions across solver are exactly the<br>
> same<br>
> > when<br>
> > > >>? ? ? ? ? ? ?I run the code.<br>
> > > >>? ? ? ? ? ? ?> I am using 0.21.3 version. What is yours?<br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?> In [13]: import sklearn<br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?> In [14]: sklearn.__version__<br>
> > > >>? ? ? ? ? ? ?> Out[14]: '0.21.3'<br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?> Serafeim<br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?>> On 9 Oct 2019, at 21:44, Beno?t Presles<br>
> > > >>? ? ? ? ? ? ?<<a href="mailto:benoit.presles@u-bourgogne.fr" target="_blank">benoit.presles@u-bourgogne.fr</a><br>
> > > >>? ? ? ? ? ? ?<mailto:<a href="mailto:benoit.presles@u-bourgogne.fr" target="_blank">benoit.presles@u-bourgogne.fr</a>><br>
> > > >>? ? ? ? ? ? ?>> <mailto:<a href="mailto:benoit.presles@u-bourgogne.fr" target="_blank">benoit.presles@u-bourgogne.fr</a><br>
> > > >>? ? ? ? ? ? ?<mailto:<a href="mailto:benoit.presles@u-bourgogne.fr" target="_blank">benoit.presles@u-bourgogne.fr</a>>>> wrote:<br>
> > > >>? ? ? ? ? ? ?>><br>
> > > >>? ? ? ? ? ? ?>> (y_pred_lbfgs==y_pred_saga).all() == False<br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> > > >>? ? ? ? ? ? ?><br>
> _______________________________________________<br>
> > > >>? ? ? ? ? ? ?> scikit-learn mailing list<br>
> > > >>? ? ? ? ? ? ?> <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a> <mailto:<br>
> > <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a>><br>
> > > >>? ? ? ? ? ? ?><br>
> > <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
> > > >>? ? ? ? ? ? ?><br>
<br>
> > > >>? ? ? ? ? ? ?_______________________________________________<br>
> > > >>? ? ? ? ? ? ?scikit-learn mailing list<br>
> > > >>? ? ? ? ? ? ?<a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a> <mailto:<br>
> > <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a>><br>
> > > >>? ? ? ? ? ? ?<a href="https://mail.python.org/mailman/listinfo/" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/</a><br>
> scikit-learn<br>
<br>
<br>
<br>
> > > >>? ? ? ? ?--<br>
> > > >>? ? ? ? ?Guillaume Lemaitre<br>
> > > >>? ? ? ? ?Scikit-learn @ Inria Foundation<br>
> > > >>? ? ? ? ?<a href="https://glemaitre.github.io/" rel="noreferrer" target="_blank">https://glemaitre.github.io/</a><br>
<br>
<br>
<br>
> > > >>? ? ?--<br>
> > > >>? ? ?Guillaume Lemaitre<br>
> > > >>? ? ?Scikit-learn @ Inria Foundation<br>
> > > >>? ? ?<a href="https://glemaitre.github.io/" rel="noreferrer" target="_blank">https://glemaitre.github.io/</a><br>
<br>
<br>
<br>
> > > >> --<br>
> > > >> Guillaume Lemaitre<br>
> > > >> Scikit-learn @ Inria Foundation<br>
> > > >> <a href="https://glemaitre.github.io/" rel="noreferrer" target="_blank">https://glemaitre.github.io/</a><br>
<br>
> > > >> _______________________________________________<br>
> > > >> scikit-learn mailing list<br>
> > > >> <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
> > > >> <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
<br>
> > > > _______________________________________________<br>
> > > > scikit-learn mailing list<br>
> > > > <a href="mailto:scikit-learn@python.org" target="_blank">scikit-learn@python.org</a><br>
> > > > <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="noreferrer" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a><br>
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Research Director, INRIA Visiting professor, McGill <br>
<a href="http://gael-varoquaux.info" rel="noreferrer" target="_blank">http://gael-varoquaux.info</a> <a href="http://twitter.com/GaelVaroquaux" rel="noreferrer" target="_blank">http://twitter.com/GaelVaroquaux</a><br>
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