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
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
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 jsseab...@gmail.com
On Fri, Nov 8, 2013 at 6:30 PM, Karol Pysniak kpysn
(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 kpysn...@gmail.com wrote:
Awesome, thanks Vlad, that's exactly what I've been looking for!
Thanks,
Karol
2013/11/8 Vlad Niculae
Hi Andy,
The issue is targeted in PR #2185
(https://github.com/scikit-learn/scikit-learn/pull/2355). There are a few
proposed ways of solving that problem, but it still needs to run a few more
benchmarks to determine the best one. Is it the problem you mean?
Thanks,
Karol
On Nov 5, 2013, at
on the most effective methods.
I found this paper / software which could serve as a reference:
http://jmlr.org/papers/v11/escalera10a.html
Mathieu
On Mon, Aug 12, 2013 at 1:27 PM, Karol Pysniak kpysn...@gmail.com wrote:
Hi All,
Currently, scikit-learn uses randomly generated codebook for
error
Hi All,
Currently, scikit-learn uses randomly generated codebook for
error-correcting output-code (line 468 in sklearn/multiclass.py). However,
there are some interesting strategies we could use in sklearn. In
particular, I would like to start from trying:
1. BCH Codes as mentioned in section
Hi All,
Is anyone currently working on issue #2022? If not, I would like to start
working on it.
Thanks,
Karol
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Get your SQL database under version control now!
Version control is standard for application code, but
/scikit-learn/scikit-learn/pull/2110
(link to #2022 is here
https://github.com/scikit-learn/scikit-learn/issues/2022)
Kyle
On Sun, Aug 4, 2013 at 7:46 PM, Karol Pysniak kpysn...@gmail.com wrote:
Hi All,
Is anyone currently working on issue #2022? If not, I would like to start
working
Hi All,
I would like to get involved in contributing to scikit-learn and I found
the issues which seem to be good starting points. Could you tell me if
anyone is currently working on Issue #2091 or Issue #2077? Would it be
possible to learn some more details about those issues?
Thanks,
Karol
Hi All,
I am new to scikit-learn, and I am very keen on starting contributing to
the project. However, I couldn't find any developer list, where I could
propose my ideas. Could you direct me to such a list or are such
discussions generally taken in the general mailing list?
Many thanks,
Karol
implementation, but I am obviously
open to any kind of suggestion. :)
Many thanks,
Karol Pysniak
-
2013/6/4 Gael Varoquaux gael.varoqu...@normalesup.org
Welcome,
This is right list.
Gaƫl
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