OML4SQL 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 OML4SQL 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, OML4SQL 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 function while regression, ranking, count, and survival are supported under the regression machine learning function.

XGBoost also supports partitioned models and internalizes the data preparation.

The SQL scoring functions supported for a classification XGBoost model are PREDICTION, PREDICTION_COST, PREDICTION_DETAILS, PREDICTION_PROBABILITY, and PREDICTION_SET.

The scoring functions supported for a regression XGBoost model are PREDICTION and PREDICTION_DETAILS.

The prediction functions return the following information:

• PREDICTION returns the predicted value.
• PREDICTION_COST returns a measure of cost for a given prediction as an Oracle NUMBER. (classification only)
• PREDICTION_DETAILS returns the SHAP (SHapley Additive exPlanation) contributions.
• PREDICTION_PROBABILITY returns the probability for a given prediction. (classification only)
• PREDICTION_SET returns the prediction and the corresponding prediction probability for each observation. (classification only)