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 --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
