There is actually an open PR to import the sample_weight changes into the
scikit-learn copy of liblinear:
https://github.com/scikit-learn/scikit-learn/pull/2784. It would appreciate
some love, or someone to executively decide that it's not worth including.
On 29 July 2014 10:36, Sean Violante <[email protected]> wrote:
> it wasn't clear from the blog post but
> are you aware that liblinear has a modification that handles sample
> weights
> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances
>
> [fyi what I would be interested in (and I am not sure this is implemented
> in that mod) is where one can aggregate the target]
> ie in dealing with categorical data one would group over the input data,
> weighting =group size and have target variable=average over group.
>
>
> On Tue, Jul 29, 2014 at 2:03 AM, <
> [email protected]> wrote:
>
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>> Today's Topics:
>>
>> 1. Re: Evaluation measure for imbalanced data (Yogesh Karpate)
>> 2. [GSoC] - Logistic Regression CV (Manoj Kumar)
>> 3. Re: RBK Kernel - Query (umang patel)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Tue, 29 Jul 2014 00:12:02 +0200
>> From: Yogesh Karpate <[email protected]>
>> Subject: Re: [Scikit-learn-general] Evaluation measure for imbalanced
>> data
>> To: [email protected]
>> Message-ID:
>> <
>> cag7mfdsbnfqmqemddfxxzouhfhpnusbivjxcf1ozyegmvzu...@mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Dear Hamed,
>> Can you share the code of "balanced accuracy" as you mentioned in last
>> mail.
>>
>>
>> On Tue, Jul 29, 2014 at 12:07 AM, Hamed Zamani <[email protected]>
>> wrote:
>>
>> > Dear Mario,
>> >
>> > Yes of course. Sorry I forgot to mention GMeans. It is also one of the
>> > measures which have been used frequently.
>> >
>> > -- Hamed
>> >
>> >
>> >
>> > On Tue, Jul 29, 2014 at 2:24 AM, Mario Michael Krell <
>> [email protected]>
>> > wrote:
>> >
>> >> Dear Hamed,
>> >>
>> >> I think it would be a good idea to also consider gmean when extending
>> >> scikit. It is the geometric mean of TNR and TPR instead of the
>> arithmetic
>> >> mean used for the balanced accuracy.
>> >>
>> >> Greets
>> >>
>> >> Mario
>> >>
>> >> On 28.07.2014, at 19:00,
>> >> [email protected] wrote:
>> >>
>> >> Dear Joel,
>> >>
>> >> Sorry for the delay. I was in a trip and I couldn't check my email.
>> >>
>> >> To the best of my knowledge and according to the kind responses in this
>> >> email thread, we cannot claim that an specific measure is better than
>> the
>> >> others for imbalanced data. In other words, there are some evaluation
>> >> measure suitable for imbalanced data and each of them has its own
>> >> advantages. Hence, choosing the best evaluation measure totally
>> depends on
>> >> the application which you are working on.
>> >>
>> >> Anyway, "Matthew's Correlation Coefficient", "AUC of ROC", "F-measure",
>> >> "Balanced Accuracy", and generally "Weighted Accuracy" have been used
>> >> frequently in the literature. Among these measures, only "balanced
>> >> accuracy" is not developed in scikit-learn and I think it is
>> worthwhile to
>> >> add it to this library. I have developed it before and if you want I
>> can
>> >> add it to the project or send it to you.
>> >>
>> >> Kind Regards,
>> >> Hamed
>> >>
>> >>
>> >>
>> >>
>> >>
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>> >>
>> >
>> >
>> >
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>>
>> --
>> Warm Regards
>> Yogesh Karpate
>> -------------- next part --------------
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>> ------------------------------
>>
>> Message: 2
>> Date: Tue, 29 Jul 2014 01:38:44 +0200
>> From: Manoj Kumar <[email protected]>
>> Subject: [Scikit-learn-general] [GSoC] - Logistic Regression CV
>> To: [email protected]
>> Message-ID:
>> <
>> cafqad-nkckkapj8r8gez37pjd35ghwkwah_bd7s-avuhrfp...@mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Hi, A update on the new Logistic Regression CV model in scikit-learn
>>
>>
>> http://manojbits.wordpress.com/2014/07/28/scikit-learn-logistic-regression-cv-2/
>>
>>
>> --
>> Regards,
>> Manoj Kumar,
>> GSoC 2014, Scikit-learn
>> Mech Undergrad
>> http://manojbits.wordpress.com
>> -------------- next part --------------
>> An HTML attachment was scrubbed...
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>> ------------------------------
>>
>> Message: 3
>> Date: Mon, 28 Jul 2014 20:02:56 -0400
>> From: umang patel <[email protected]>
>> Subject: Re: [Scikit-learn-general] RBK Kernel - Query
>> To: [email protected]
>> Message-ID:
>> <
>> camgx1excdd36fbnhxm_vmjry6adccnaesc3zpnxs5ojwn36...@mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Hi Andy ,
>>
>> I nvr got answer . Could you please re- answer if possible . I will really
>> appraciate it .
>>
>> Thank you.
>>
>>
>>
>> On Mon, Jul 28, 2014 at 2:53 PM, Andy <[email protected]> wrote:
>>
>> > Please do not repost.
>> > You got an answer on the issue if I recall correctly.
>> > If you want to know more, pick up a textbook on machine learning, such
>> as
>> > ESL (free pdf:
>> > http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf
>> ),
>> > Kevin Murpy's book or the Bishop.
>> >
>> >
>> > On 07/27/2014 08:22 PM, umang patel wrote:
>> >
>> > Hello all ,
>> >
>> > I asked the following question on "Issues" and I was advised to mail
>> on
>> > the following email id I i have furthur queries .
>> >
>> > "
>> > Is it possible to get weight of features in rbf kernel .
>> >
>> > It is written under coeff_ that it is possible only with linear kernel .
>> > Is it mathematically possible to get for rfb kernel , if yes then how ?
>> > "
>> >
>> >
>> > Could and one please direct me to relevant paper or please explain why
>> it
>> > is not possible to get coeff_ for rbf kernel .
>> >
>> > Thank you.
>> >
>> >
>> > On Wed, Jul 23, 2014 at 8:33 AM, umang patel <[email protected]>
>> > wrote:
>> >
>> >> Hello all ,
>> >>
>> >> I asked the following question on "Issues" and I was advised to mail
>> on
>> >> the following email id I i have furthur queries .
>> >>
>> >> "
>> >> Is it possible to get weight of features in rbf kernel .
>> >>
>> >> It is written under coeff_ that it is possible only with linear kernel
>> .
>> >> Is it mathematically possible to get for rfb kernel , if yes then how ?
>> >> "
>> >>
>> >>
>> >> Could and one please direct me to relevant paper or please explain why
>> >> it is not possible to get coeff_ for rbf kernel .
>> >>
>> >> Thank you.
>> >>
>> >
>> >
>> >
>> >
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