fee1181803
I use the online prediction function(`inline void Predict(const SparseBatch::Inst &inst, ... ) const;`), the results obtained are different from the results of the batch prediction function(` virtual void Predict(DMatrix* data, ...) const = 0`). After the investigation found that the online prediction function using the `base_score_` parameters, and the batch prediction function is not used in this parameter. It is found that the `base_score_` values are different when the same model file is loaded many times. ``` 1st times：base_score_: 6.69023e-21 2nd times：base_score_: -3.7668e+19 3rd times：base_score_: 5.40507e+07 ``` Online prediction results are affected by `base_score_` parameters. After deleting the if condition(`if (out_preds->size() == 1)`) , the online prediction is consistent with the batch prediction results, and the xgboost prediction results are consistent with python version. Therefore, it is likely that the online prediction function is bug
eXtreme Gradient Boosting
Documentation | Resources | Installation | Release Notes | RoadMap
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
What's New
- XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
- Story and Lessons Behind the Evolution of XGBoost
- Tutorial: Distributed XGBoost on AWS with YARN
- XGBoost brick Release
Ask a Question
- For reporting bugs please use the xgboost/issues page.
- For generic questions or to share your experience using XGBoost please use the XGBoost User Group
Help to Make XGBoost Better
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
- Check out call for contributions and Roadmap to see what can be improved, or open an issue if you want something.
- Contribute to the documents and examples to share your experience with other users.
- Add your stories and experience to Awesome XGBoost.
- Please add your name to CONTRIBUTORS.md and after your patch has been merged.
- Please also update NEWS.md on changes and improvements in API and docs.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington, see also the Project Page at UW.