The GRADBOOST Procedure

The GRADBOOST procedure creates a predictive model called a gradient boosting model in SAS Viya. A gradient boosting model consists of multiple decision trees. A predictive model defines a relationship between input variables and a target variable. The purpose of a predictive model is to predict a target value from inputs. The GRADBOOST procedure creates the model by using training data in which the target values are known. The model can then be applied to observations in which the target is unknown. If the predictions fit the new data well, the model is said to generalize well. Good generalization is the primary goal of predictive tasks. A predictive model might fit the training data well but generalize poorly.

A decision tree is a type of predictive model that has been developed independently in the statistics and artificial intelligence communities. Based on the boosting method in Hastie, Tibshirani, and Friedman (2001) and Friedman (2001), the GRADBOOST procedure creates a predictive model by fitting a set of additive trees.

For more information about training a gradient boosting model, see the section Boosting.