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 (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.
Binary packages can be installed with the high-level tool pkgin (which can be installed with pkg_add) or pkg_add(1) (installed by default). The NetBSD packages collection is also designed to permit easy installation from source.
The pkg_admin audit command locates any installed package which has been mentioned in security advisories as having vulnerabilities.
Please note the vulnerabilities database might not be fully accurate, and not every bug is exploitable with every configuration.
Problem reports, updates or suggestions for this package should be reported with send-pr.