XGBoost is an open-source gradient boosting framework library for C++, Java, Python, R, Julia, Perl, and Scala. The library works on Linux, Windows, and macOS. Developers love it for its accuracy, efficiency, and feasibility.
Benefits and features
- Accepts sparse input for tree booster and linear booster
- Supports customized objective and evaluation functions
- Its optimized data structure, DMatrix, improves its performance and efficiency
- Provides support for GPUs
- Supports multiple programming languages
- Supports regression, classification, ranking, and user-defined objectives
- Supports distributed computing with Kubernetes and Dask
- Ability to implement callbacks
- Can implement early stopping during model training
XGBoost API is simple to use and provides good results with default parameters. It also runs fast even on a large dataset.
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