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, Flink and DataFlow
copied from cf-staging / py-xgboost-cpu
conda install conda-forge::py-xgboost-cpuconda install conda-forge/label/cf201901::py-xgboost-cpuconda install conda-forge/label/cf202003::py-xgboost-cpuconda install conda-forge/label/gcc7::py-xgboost-cpu
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.