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dmlc / xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
https://xgboost.readthedocs.io/en/stable/
Apache License 2.0
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Quantile Regression and Support for Prediction Intervals #1433

Closed nnorton24 closed 2 weeks ago

nnorton24 commented 8 years ago

I know that sklearn.ensemble.GradientBoostingRegressor supports quantile regression and the production of prediction intervals. Are there any plans for the XGBoost package to offer similar support?

hetong007 commented 8 years ago

Quantile regression is currently not supported.

It can be achieved by defining the objective function on user's side: https://github.com/dmlc/xgboost/blob/master/R-package/demo/custom_objective.R

arbackus commented 7 years ago

Quantile regression with xgboost requires custom gradient and hessian functions. Here is an implementation in Python: http://www.bigdatarepublic.nl/regression-prediction-intervals-with-xgboost/

joseortiz3 commented 6 years ago

Note that implementation is not very useful for most users, since it specifies grid-searching three parameters (very costly) to get a quantile estimate. In sklearn, you define only the quantile value and you are given a very robust and reliable estimate of that quantile.

Sandy4321 commented 2 years ago

any progress ?

link above is broken http://www.bigdatarepublic.nl/regression-prediction-intervals-with-xgboost/

trivialfis commented 1 year ago

Related https://github.com/unit8co/darts/issues/1134 .

BraulioPi commented 1 year ago

??

trivialfis commented 2 weeks ago

Supported by the Python package, and R package (still in master).