Hello German, Firstly, I apologize for the delayed response. I am currently in the midst of midterm exams, which have kept me quite busy. Definitely, I will be using the knowledge of the previous works to build my code. I also did find many errors and corrections which needed to be rectified/changed. I will work on those also and in-case I find it hard to resolve the errors based on the old code, I will make the implementation from scratch.
Thank you for your time. Best regards, Adarsh On Mon, Mar 20, 2023 at 9:00 PM Germán Lancioni <germanlanci...@gmail.com> wrote: > Hi Adarsh, > > Thanks for reaching out. I like your topic idea. Have you seen the > previous work in XGBoost? It would be beneficial if you capitalized on > pre-existing work instead of starting from scratch, unless you see a > problem with that. Please take a look here: > https://github.com/RishabhGarg108/GSoC-Final-Eval > > Thanks, > German > ------------------------------ > *From:* Adarsh Santoria <adarshsanto...@gmail.com> > *Sent:* Sunday, March 19, 2023 12:23 PM > *To:* mlpack@lists.mlpack.org <mlpack@lists.mlpack.org> > *Subject:* [mlpack] GSoC Proposal: Improvement in Ensemble Trees with > XGBoost Implementation > > Dear mlpack community, > > My name is Adarsh Santoria, and I am a sophomore at IIT Mandi, India. I am > writing to submit my proposal for the GSoC project on improving ensemble > trees with XGBoost implementation. You can access the document through this > link: > https://docs.google.com/document/d/1mQx5e7thE42zIlEPO2U5aUkk4sZfvDZxBWtTYgytrNY/edit?usp=sharing, > which outlines my project plan and timeline in detail. XGBoost is a machine > learning algorithm that uses decision trees as base learners, known for its > high accuracy, interpretability, scalability, feature importance, and > robustness to noisy or incomplete data. Implementing XGBoost in mlpack is a > necessary step towards enhancing the performance of ensemble trees, making > it an important contribution to the mlpack community. > > In summary, my proposal includes the following: > ● Implementing Random Forest Regressor method and adding tests > ● Parallelizing decision tree, random forest and xgboost with OpenMP > ● Adding bindings for the decision tree, random forest and xgboost > ● Adding the XGBoost Classifier and Regressor along with some split > methods and loss functions. > ● Adding tutorials and sample code snippets > > I believe that with my skills and experience, I can make significant > contributions to mlpack and enhance the performance of ensemble trees with > XGBoost implementation. > Thank you for considering my proposal for the GSoC project. > > Best regards, > Adarsh Santoria > Github link: https://github.com/AdarshSantoria >
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