1.0 - 8.00 - XGBoost Functions - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
Release Date
May 2019
Content Type
Programming Reference
Publication ID
English (United States)

The XGBoost function trains a classification model using gradient boosting with decision trees as the base-line classifier and has a corresponding predict function, XGBoostPredict.

In gradient boosting, each iteration fits a model to the residuals (errors) of the previous iteration. It also provides a general framework for adding a loss function and a regularization term.

The ML Engine implementation of the XGBoost algorithm includes:
  • Loss functions:
    • Binomial (for binary classification)
    • Softmax (for multiple-class classification)
  • L2 regularization
  • Shrinkage
  • Column subsampling

Row subsampling is implemented by randomly partitioning the input data set among the available vworkers. By distributing the input data set across vworkers, we train multiple gradient boosting trees in parallel, each on a subset of data. The results are combined to create a final prediction by majority vote.

You can use the XGBoost functions to create predictions input for the Receiver Operating Characteristic (ROC) function.

For a general description of gradient boosting, see https://statweb.stanford.edu/~jhf/ftp/trebst.pdf. For more details about the XGBoost algorithm, see http://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf.

Function Description
XGBoost Takes training data set in dense or sparse format and uses gradient boosting to create strong classifying model.
XGBoostPredict Applies model output by XGBoost to a new data set, outputting predicted labels for each data point.