[mlpack] Potential Proposal for GSoC 2021

[Omar Shrit]

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

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