XGBoost
An open-source gradient boosting library for machine learning on your own hardware or distributed clusters, with Python and R support. Apache 2.0 licensed.
XGBoost is a machine learning library for training and running gradient-boosted tree models on your own hardware. Data scientists use it for classification, regression and ranking, with parallel computation for larger workloads. The code is licensed under Apache 2.0.
The library has a C++ implementation and interfaces for Python, R, Java, Scala and C. Python users can install it with pip install xgboost and fit models through its native API or scikit-learn-compatible estimators.
XGBoost supports single-machine training and distributed workflows, including Spark and Dask. CPU execution is available, while compatible NVIDIA GPUs can accelerate supported operations. GPU and distributed capabilities depend on the operating system, package build and chosen integration.
Trained models can be saved and loaded for prediction, letting teams use the same model in experiments and application software. Its tree-boosting focus makes it useful for structured datasets where developers want to compare predictive models without building a neural network.
