XGBoost4J could integrate with spark from 1.6 version. Currently I am using spark 1.5.2. Can I use XGBoost instead of XGBoost4j.
Will both provides same result. Thanks, Selvam R +91-97877-87724 On Mar 15, 2016 9:23 PM, "Nan Zhu" <zhunanmcg...@gmail.com> wrote: > Dear Spark Users and Developers, > > We (Distributed (Deep) Machine Learning Community (http://dmlc.ml/)) are > happy to announce the release of XGBoost4J ( > http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html), > a Portable Distributed XGBoost in Spark, Flink and Dataflow > > XGBoost is an optimized distributed gradient boosting library designed to > be highly *efficient*, *flexible* and *portable*.XGBoost provides a > parallel tree boosting (also known as GBDT, GBM) that solve many data > science problems in a fast and accurate way. It has been the winning > solution for many machine learning scenarios, ranging from Machine Learning > Challenges ( > https://github.com/dmlc/xgboost/tree/master/demo#machine-learning-challenge-winning-solutions) > to Industrial User Cases ( > https://github.com/dmlc/xgboost/tree/master/demo#usecases) > > *XGBoost4J* is a new package in XGBoost aiming to provide the clean > Scala/Java APIs and the seamless integration with the mainstream data > processing platform, like Apache Spark. With XGBoost4J, users can run > XGBoost as a stage of Spark job and build a unified pipeline from ETL to > Model training to data product service within Spark, instead of jumping > across two different systems, i.e. XGBoost and Spark. (Example: > https://github.com/dmlc/xgboost/blob/master/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/DistTrainWithSpark.scala > ) > > Today, we release the first version of XGBoost4J to bring more choices to > the Spark users who are seeking the solutions to build highly efficient > data analytic platform and enrich the Spark ecosystem. We will keep moving > forward to integrate with more features of Spark. Of course, you are more > than welcome to join us and contribute to the project! > > For more details of distributed XGBoost, you can refer to the > recently published paper: http://arxiv.org/abs/1603.02754 > > Best, > > -- > Nan Zhu > http://codingcat.me >
