XGBoost Parameters — xgboost 2.2.0-dev documentation
XGBoost Parameters Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters. General parameters relate to which booster we are using to do boosting, commonly tree or linear model Booster parameters depend on which booster you have chosen Learning task parameters decide on the learning scenario. For example, regression tasks may use
XGBoost Documentation — xgboost 2.1.0-dev documentation
XGBoost Documentation 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 envi
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