Hi everyone, Here I will be proposing a possible project for GSOC 2021. This project is inspired by #2421 <https://github.com/mlpack/mlpack/issues/2421>.
Majority of the machine learning libraries (scikit-learn, xgboost, catboost) follow a ".fit" and ".predict" type interface. Where the models are trained by doing something like "model.fit(X,y)" and predictions are made using "model.predict(X,y)" (inside python). Unfortunately mlpack does not support such an interface which makes it difficult for people to get familiar (people who use mlpack through bindings and not c++ directly) with mlpack. So I would like to propose this as an idea for GSOC 2021 that would involve changing the bindings to support this interface. Since I spend most of my time exploring various ML libraries inside python (most of which support this interface), it would be an amazing experience for me to work on this so that mlpack can also support this interface. I am familiar with the mlpack binding system and have worked on #2787 <https://github.com/mlpack/mlpack/pull/2787> and currently working on #2868 <https://github.com/mlpack/mlpack/pull/2868>. Apart from these PR's there are more PR's that I have worked on but these are closely related to the project. I request all the mentors to see if this can be a good GSOC project and if anyone would like to mentor this. Feedbacks are welcome from everyone. Regards Nippun Sharma
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