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From Meihua Wu <rotationsymmetr...@gmail.com>
Subject Spark Implementation of XGBoost
Date Mon, 26 Oct 2015 18:42:53 GMT
Hi Spark User/Dev,

Inspired by the success of XGBoost, I have created a Spark package for
gradient boosting tree with 2nd order approximation of arbitrary
user-defined loss functions.

https://github.com/rotationsymmetry/SparkXGBoost

Currently linear (normal) regression, binary classification, Poisson
regression are supported. You can extend with other loss function as
well.

L1, L2, bagging, feature sub-sampling are also employed to avoid overfitting.

Thank you for testing. I am looking forward to your comments and
suggestions. Bugs or improvements can be reported through GitHub.

Many thanks!

Meihua

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