Also, does it support categorical feature? Sincerely,
DB Tsai ---------------------------------------------------------- Web: https://www.dbtsai.com PGP Key ID: 0xAF08DF8D On Mon, Oct 26, 2015 at 4:06 PM, DB Tsai <[email protected]> wrote: > Interesting. For feature sub-sampling, is it per-node or per-tree? Do > you think you can implement generic GBM and have it merged as part of > Spark codebase? > > Sincerely, > > DB Tsai > ---------------------------------------------------------- > Web: https://www.dbtsai.com > PGP Key ID: 0xAF08DF8D > > > On Mon, Oct 26, 2015 at 11:42 AM, Meihua Wu > <[email protected]> wrote: >> 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: [email protected] >> For additional commands, e-mail: [email protected] >> --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
