XGBoost© is an advanced implementation of a gradient boosting algorithm. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. XGBoost is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoost-AS node in watsonx.ai Studio exposes the core features and commonly used parameters. The XGBoost-AS node is implemented in Spark.

For more information about boosting algorithms, see the XGBoost Tutorials. ¹

Note that the XGBoost cross-validation function is not supported in watsonx.ai Studio. You can use the Partition node for this functionality. Also note that XGBoost in watsonx.ai Studio performs one-hot encoding automatically for categorical variables.

¹ "XGBoost Tutorials." Scalable and Flexible Gradient Boosting. Web. © 2015-2016 DMLC.