Overview
XGBoost¶
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.
Software Components¶
Table 1 lists the necessary software components. The descending row order represents the install sequence. The recommended component version and download location are also provided.
Table 1: Software Components | Component| Version | | :--- | :----: | | Debian | 10 | | Python | 3.10 | | scikit-learn | 1.1.1 | | xgboost | 1.6.1 | | pandas | pd | | openpyxl | opxl | | tqdm | tqdm | | requests | res |
Configuration Snippets¶
This section contains code snippets on build instructions for software components.
Note: Common Linux utilities, such as docker, git, wget, will not be listed here. Please install on demand if it is not provided in base OS installation.
DEBIAN¶
PYTHON¶
apt-get update && curl -Ls https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tgz -o Python-3.10.0.tgz && tar -xzvf Python-3.10.0.tgz && cd Python-3.10.0 && ./configure --enable-optimizations && make -j8 && make install