>> In my understanding, R^2 could be thought as the square of Correlation
>> coefficient thus R^2 can't be negative. Is there something wrong, who
I agree that this can be confusing. However, R^2 is not always calculated as
R*R, e.g., see http://biomet.oxfordjournals.org/content/78/3/691.full.pdf+html
<http://biomet.oxfordjournals.org/content/78/3/691.full.pdf+html>
There is also something called "adjusted R^2" which can result in negative R^2
values. But as far as I know those are typically set to 0 in most application.
Best,
Sebastian
> On May 5, 2015, at 2:34 PM, Andreas Mueller <t3k...@gmail.com> wrote:
>
> In extreme cases, R^2 can be negative.
> If you'd like to contribute, have a look at the guidelines here:
> http://scikit-learn.org/dev/developers/index.html
> <http://scikit-learn.org/dev/developers/index.html>
>
>
> On 05/05/2015 06:59 AM, Yao wrote:
>> Hi, all,
>> Congratulations on the acceptance of your GSoC proposal!
>> I'm a Master student from Peking University, and I have a great interest
>> on scikit-learn because of frequency use.
>> And I also want to contribute to the community, or try to fix some bugs.
>> Could you count me in?
>>
>> By the way, I have met a problem, Unlike most other scores, R^2 score
>> may be negative (it need not actually be the square of a quantity R).
>> In my understanding, R^2 could be thought as the square of Correlation
>> coefficient thus R^2 can't be negative. Is there something wrong, who
>> can tell me?
>>
>> Thanks,
>> Yao.
>>
>>
>>
>>
>> At 2015-05-04 19:02:36, "Michael Eickenberg" <michael.eickenb...@gmail.com>
>> <mailto:michael.eickenb...@gmail.com> wrote:
>> Dear Artem,
>>
>> congratulations on the acceptance of your GSoC proposal! I am certain there
>> will be a very interesting summer ahead of us. Kyle and I are excited to be
>> mentors and will do our best to provide all the guidance necessary for your
>> project to succeed. It is very rich and will be a great addition to the
>> codebase.
>>
>> Your blog post <http://barmaley-exe.blogspot.ru/2015/05/introduction.html>
>> on the gists of the methods is written in a very understandable way and
>> permits a good overview of the topics you are going to address in depth. It
>> shows that you have the right intuitions, and are ready to delve into the
>> intricacies of the methods [1]. Take advantage of the next weeks to do so!
>> Let's make sure we hit the ground running at the end of this warm-up phase.
>>
>> As for your next plans, sketching the algorithms in very high level
>> pseudo-code is of course an excellent idea and can be a next blog post.
>> After this, you can zoom in on the details of how each pseudo-code step can
>> be implemented. If you get the level of detail right, I recommend the Python
>> language to describe your algorithms ;) -- what I mean is that getting a
>> minimal version of the algorithm to work, just as a function, not a sklearn
>> estimator, is a valuable baseline to have, and it usually deepens the
>> understanding as well.
>>
>> As for the API questions, it is of course quite essential to remain
>> conscious at all times of the issues that have been identified in prior
>> discussion and to think of ways to add a metric learning module without
>> succumbing to excessive feature creep. My hunch is that given some working
>> minimal versions of the algorithms, we can perhaps crystallize out what is
>> absolutely necessary in terms of additions, so I would prefer that order of
>> priorities. There is also some work to be done in identifying other parts of
>> scikit-learn that already deal with (dis-)similarity type data (cf eg the
>> kernels defined in the PR for gaussian processes) and see how these can be
>> made to work in a consistent way.
>>
>> A crucial aspect that we need to figure out is "what is a good outcome?": Of
>> course we would like to have some PRs merged at the end of summer, yes. But
>> what makes a concrete implementation good? Speed (with respect to what)?
>> Readability? Maintainability (yes please!)? Elegance (what does that even
>> mean?)?
>>
>> It may be helpful if you could devise a more fine-grained timeline
>> <https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2015-Proposal:-Metric-Learning-module#timeline>
>> for the community bonding period than what is currently stated on the wiki
>> page. How about blogging your progress in understanding? Writing things down
>> for others to understand is a very good way of identifying any doubts you
>> may have on particular aspects. A mini blog-entry at the end of each week
>> simply recounting what has been done and also what has been discussed will
>> also yield an effective overview of ongoing topics.
>>
>> In the meantime, please don't hesitate to bombard us, the other mentors, and
>> the list with any questions you may have.
>>
>> Michael
>>
>> [1] http://i.imgur.com/at3vIHh.png <http://i.imgur.com/at3vIHh.png>
>>
>>
>>
>>
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