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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