Hi Raghav,
Thanks a lot for your reply. That helps so much.
I understand that the proposal should be specific to a module but right now
I am not sure which of these implementation are the most sought-after. I
will update the proposal based on the inputs.
I also have looked at the stalled PRs of Metric learning NCA and Matrix
Completion for missing values, but they have heavy on math. If they are of
utmost importance, I would gladly spend time to read through the reference
papers.
I would really appreciate any other feedback on this proposal.
Thank you again for your time !
Best regards,
Maniteja.
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On 23 Mar 2016 5:27 pm, "Raghav R V" <rag...@gmail.com> wrote:
> Hey Maniteja,
>
> Having taken a quick look at the list... my thoughts -
>
> * The KNN imputation is an important addition that got stalled.
> * The semi-supervised NB with EM seems like a good addition, Olivier,
> Larsmans (and Joel?) have to comment on whether it should be a priority.
> * The haversine metric is tagged "easy".
> * "Meta-estimator for semi-supervised learning" is not hard but I believe
> is API heavy and would involve devoting considerable amount of time for API
> discussions...
> * "Label power set multilabel classification strategy" doesn't look like a
> priority.
> * I am not very sure if infomax ICA had good interest among core devs.
> * *I think* People were pretty interested in Metric Learning NCA and
> Matrix completion with missing values, but I believe they are math heavy.
> Make sure you can handle that! Ping Olivier if you need more information.
>
> Also please note that the proposal needs to have a central theme like
> "Improvements in linear models" or "Improvements in tree models" and your
> should propose to complete the stalled PRs under that theme...
>
> Thanks for the mail! Good luck on your proposal! Please note that the
> deadline is on 25th of this month!
>
> Raghav
>
> On Mon, Mar 21, 2016 at 7:35 PM, Maniteja Nandana <
> maniteja.modesty...@gmail.com> wrote:
>
>> Hello everyone,
>>
>> My name is Maniteja, a senior year computer science student from India (
>> github <https://github.com/maniteja123>)
>> It was been a wonderful learning opportunity contributing to the library
>> for the past few months and would like to thank everyone for their support
>> and patiently answering my questions. I am really eager to contribute more
>> to my best abilities. Since it was proposed to work on existing PRs, I have
>> also added better detailed version at here
>> <https://github.com/maniteja123/scikit-learn/wiki/Various-enhancements-to-scikit-learn>
>>
>> I wanted to seek feedback on the following issues and PRs . If any of the
>> authors of the following PRs are interested to work on their PRs please let
>> me know and I am sorry for not asking prior permission since I couldn't
>> contact each of you and also didn't want to create noise by commenting on
>> all the PRs. Hope you understand. If it is okay for me to try working on
>> these, please let me know your opinions and suggestions.
>>
>> Semi-supervised Naive Bayes using Expectation Maximization #430
>> <https://github.com/scikit-learn/scikit-learn/pull/430>
>> Meta estimator for self trained model #1243
>> <https://github.com/scikit-learn/scikit-learn/issues/1243>
>> Use Bayesian priors in Nearest Neighbors classifier #399
>> <https://github.com/scikit-learn/scikit-learn/issues/399> #970
>> <https://github.com/scikit-learn/scikit-learn/pull/970%5C>
>> Classifier Chain for multi-label problems PRs: #3727
>> <https://github.com/scikit-learn/scikit-learn/pull/3727> #4759
>> <https://github.com/scikit-learn/scikit-learn/issues/4759>
>> Label power set multilabel classification strategy PRs: #2461
>> <https://github.com/scikit-learn/scikit-learn/pull/2461>
>> Multioutput bagging #4848
>> <https://github.com/scikit-learn/scikit-learn/pull/4848>
>> Added 'average' option to passive aggressive classifier/regressor. #4939
>> <https://github.com/scikit-learn/scikit-learn/pull/4939>
>> Add "grouped" option to Scaler classes: #4963
>> <https://github.com/scikit-learn/scikit-learn/pull/4963>
>> Metric precision at k score #4975
>> <https://github.com/scikit-learn/scikit-learn/4975>
>> Implement haversine metric in pairwise #4458
>> <https://github.com/scikit-learn/scikit-learn/pull/4458> #4453
>> <https://github.com/scikit-learn/scikit-learn/issues/4453>
>> Add KNN strategy for imputation #4844
>> <https://github.com/scikit-learn/scikit-learn/pull/4844>
>> Add resample to preprocessing. #1454
>> <https://github.com/scikit-learn/scikit-learn/pull/1454> #6568
>> <https://github.com/scikit-learn/scikit-learn/issues/6568>
>> Added metrics support for multiclass-multioutput classification #3681
>> <https://github.com/scikit-learn/scikit-learn/pull/3681>
>> random neural network algorithm #4703
>> <https://github.com/scikit-learn/scikit-learn/pull/4703>
>>
>> Thank you for your time and waiting to hear back from you !
>>
>> Yours sincerely,
>> Maniteja.
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