Hi German, Thanks for the feedback. I agree. It is better to commit to completely implement one algorithm than to partially implement many. Will consider this in my proposal.
Regards, Anush Kini On Mon, Mar 15, 2021 at 11:14 PM Germán Lancioni <[email protected]> wrote: > Hi Anush, > > This is a great area to work on. As Omar mentioned, a good scope maximizes > and focuses your GSoC effort. If you notice that the available GSoC time is > not enough, I would recommend implementing just 1 of the algorithms, e.g. > XGB so you can concentrate on the completeness of it instead of stretching > your time with 3. > > Looking forward to your proposal, very exiting! > > Regards, > German > > ------------------------------ > *From:* mlpack <[email protected]> on behalf of Anush Kini < > [email protected]> > *Sent:* Monday, March 15, 2021 09:14 AM > *To:* Omar Shrit <[email protected]> > *Cc:* [email protected] <[email protected]> > *Subject:* Re: [mlpack] Potential Proposal for GSoC 2021 > > 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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