Hello,
We're contributors to the open source project Metacademy (
http://www.metacademy.org http://github.com/metacademy), which is creating
a curated web of machine learning concepts. The idea is that Metacademy can
tell users how to efficiently learn a concept and all of its necessary
prerequisi
Yes, it would be perfect. Do you have any work planned or schedule on this
problem? Let me know if there is something I could help with.
Thanks,
Karol
2013/11/8 Vlad Niculae
> It should be written in such a way so that you can add more benchmarks
> with a PR to that repo (the master branch) an
It should be written in such a way so that you can add more benchmarks
with a PR to that repo (the master branch) and it should "just work".
Many parts of the framework are still hackish though.
Yours,
Vlad
On Fri, Nov 8, 2013 at 7:53 PM, Karol Pysniak wrote:
> Awesome, thanks Vlad, that's exact
Awesome, thanks Vlad, that's exactly what I've been looking for!
Thanks,
Karol
2013/11/8 Vlad Niculae
> We have an instance of vbench continuously running [1] that I did as a
> GSoC project last year.
>
> For some reason it seems that the links don't generate properly now,
> but it still works
Looks good, but I was more interested if we want to have a single script or
set of scripts that would produce a single number that could used to
compare changes. What do you think?
Thanks,
Karol
2013/11/8 Skipper Seabold
> On Fri, Nov 8, 2013 at 6:30 PM, Karol Pysniak wrote:
> > Hi All,
> >
>
We have an instance of vbench continuously running [1] that I did as a
GSoC project last year.
For some reason it seems that the links don't generate properly now,
but it still works (though all data got lost in a jenkins setup
incident this summer).
Here are some linear model benchmarks for exam
On Fri, Nov 8, 2013 at 6:30 PM, Karol Pysniak wrote:
> Hi All,
>
> Has there any been discussion on adding some automated benchmarks for both
> speed and accuracy of the algorithms we have? I think it would very
> interesting if such a script could be automatically executed after every
> commit so
Hi All,
Has there any been discussion on adding some automated benchmarks for both
speed and accuracy of the algorithms we have? I think it would very
interesting if such a script could be automatically executed after every
commit so that we could follow the performance of scikit-learn or, at
leas
Hi All,
I've added some new ECOC some time ago. Would it be possible to have some
review and feedback? Also, would you recommend any datasets that could be
used for verification? I am especially concerned about what type and size
of data sets I should use.
I would appreciate any help and suggesti
Did anyone work on this problem (exceptions raised by classifiers in
grid search) since? I would be happy to do some work to fix this
problem, but would need some advice.
It seems to me like the easiest way around the issue is to wrap the call
to clf.fit() in a try statement and catch the exceptio
On Fri, Nov 08, 2013 at 11:56:24AM +0100, Olivier Grisel wrote:
> In retrospect I would have prefered it named something explicit like
> "regularization" or "l2_reg" instead of "alpha".
Agreed.
> Still I like the (alpha, l1_ratio) parameterization better over the
> (l2_reg, l1_reg) parameter set
just a remark in LogisticRegression you can use L1 and L2 reg and
there is a single param that is alpha.
It's not trivial to have a consistent naming for regularization param.
In SVC it is C as it's the common
naming... but it corresponds to 1/l2_reg with what you suggest...
Alex
We cannot use lambda as parameter name because it is a reserved
keyword of the python language (for defining anonymous functions).
This is why used "alpha" instead of "lambda" for the ElasticNet /
Lasso model initially and then this notation was reused in more
recently implemented estimators such a
SGDClassifier adopted the parameter names of ElasticNet (which has been
around in sklearn for longer) for consistency reasons.
I agree that we should strive for concise and intuitive parameter names
such as ``l1_ratio``.
Naming in sklearn is actually quite unfortunate since the popular R package
Just my 0.02$ as a user: I was also a confused/put-off by `alpha` and
`l1_ratio` when I first explored SGDClassifier, I found those names to
be pretty inconsistent --- plus I tend to call my regularization
parameters `lambda` and use `alpha` for learning rates. I'm sure other
people associate y
And lambda is a reserved keyword in Python ;-)
On Fri, Nov 8, 2013 at 4:59 PM, Olivier Grisel wrote:
> 2013/11/7 Mathieu Blondel :
> >
> > On Fri, Nov 8, 2013 at 12:28 AM, Vlad Niculae
> wrote:
> >>
> >> I feel like this would go against "explicit is better than implicit",
> >> but without it g
Re: the discussion we had at PyCon.fr, I noticed that the internal
elastic net coordinate descent functions are parametrized with
`l1_reg` and `l2_reg`, but the exposed classes and functions have
`alpha` and `l1_ratio`. Only yesterday there was somebody on IRC who
couldn't match Ridge with Elastic
About the LBFGS-B residuals (non-)issue I was probably confused by the
overlapping on the plot and mis-interpreted the location of the PG-l1
and PG-l2 curves.
--
Olivier
--
November Webinars for C, C++, Fortran Developer
2013/11/7 Mathieu Blondel :
>
> On Fri, Nov 8, 2013 at 12:28 AM, Vlad Niculae wrote:
>>
>> I feel like this would go against "explicit is better than implicit",
>> but without it grid search would indeed be awkward. Maybe:
>>
>> if self.alpha_coef == 'same':
>> alpha_coef = self.alpha_comp
>>
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