About XGBoost

Oracle's XGBoost prepares training data, builds and persists a model, and applies the model for classification and regression.

Oracle Machine Learning for SQL XGBoost is a scalable gradient tree boosting system that supports both classification and regression. It makes available the open source gradient boosting framework.

You can use XGBoost as a stand-alone predictor or incorporate it into real-world production pipelines for a wide range of problems such as ad click-through rate prediction, hazard risk prediction, web text classification, and so on.

The Oracle Machine Learning for SQL XGBoost algorithm takes three types of parameters: general parameters, booster parameters, and task parameters. You set the parameters through the model settings table. The algorithm supports most of the settings of the open source project.

Through XGBoost, Oracle Machine Learning for SQL supports a number of different classification and regression specifications, ranking models, and survival models. Binary and multiclass models are supported under the classification machine learning technique while regression, ranking, count, and survival are supported under the regression machine learning technique.

XGBoost also supports partitioned models and internalizes the data preparation. Currently, XGBoost is available only on Oracle Database Linux platform.