XGBoost Tree node
XGBoost Tree© is an advanced implementation of a gradient boosting algorithm with a tree model as the base model. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. XGBoost Tree is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoost Tree node in SPSS® Modeler exposes the core features and commonly used parameters. The node is implemented in Python.
For more information about boosting algorithms, see the XGBoost Tutorials available at http://xgboost.readthedocs.io/en/latest/tutorials/index.html. 1
Note that the XGBoost cross-validation function is not supported in SPSS Modeler. You can use the SPSS Modeler Partition node for this functionality. Also note that XGBoost in SPSS Modeler performs one-hot encoding automatically for categorical variables.
1 "XGBoost Tutorials." Scalable and Flexible Gradient Boosting. Web. © 2015-2016 DMLC.