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 > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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