[mlpack] Potential Proposal for GSoC 2021

[Anush Kini]

Hi Omar,

Thank you for the inputs.
What you said makes complete sense to me.

I will look towards prioritising algorithm correctness, detailed
documentation and tutorials over implementing multiple features.
Additionally, will highlight proof of concept through sample codes and
metrics in my proposal.

Thanks & Regards,
Anush Kini

On Mon, Mar 15, 2021 at 3:43 PM Omar Shrit <omar at shrit.me> wrote:

> Hello Anush,
>
> XGBoost, LightGBM and CatBoost algorithms will be a great addition for
> mlpack this year. Since GSoC is shorter, I would concentrate on these
> algorithms, with relative tests and examples.
>
> You need to demonstrate in your proposal, that you have a good knowledge
> of decision tree algorithms. As always a good starting point is a proof
> of concept with relative benchmarks.
>
> These are my suggestions, hope you find this helpful.
>
> Thanks,
>
> Omar
>
> On 03/14, Anush Kini wrote:
> > Hi Mlpack team,
> >
> > I am Anush Kini. My GitHub handle is Abilityguy
> > <https://github.com/Abilityguy>.
> >
> > I have been getting familiar with the code base for the last couple of
> > months.
> > I am planning to apply for GSoC 2021 and wanted some feedback on my
> project
> > proposal for the same.
> >
> > I am building on the 'Improve mlpack's tree ensemble support' idea from
> the
> > wiki.
> > I would like to implement XGBoost and LightGBM algorithms. If the
> schedule
> > permits, I will look towards implementing CatBoost too.
> >
> > Additionally, I would like to work on bringing some additional features
> to
> > the ensemble suite:
> > 1. I would like to dip into 2619
> > <https://github.com/mlpack/mlpack/issues/2619> which aims to implement
> > regression support to Random Forests.
> > 2. Implementing methods to get the impurity based feature importance
> > similar to the one in scikit-learn
> > <
> https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.feature_importances_
> >
> > .
> >
> > Finally, I plan to supplement any new features implemented with tutorials
> > in mlpack/examples <https://github.com/mlpack/examples>.
> > Looking forward to hearing your opinions and suggestions.
> >
> > Thanks & Regards,
> > Anush Kini
>
> > _______________________________________________
> > mlpack mailing list
> > mlpack at lists.mlpack.org
> > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
>
>
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