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