In this post, Michael Lamm, a senior research statistician developer in Advanced Analytics R&D at SAS, provides an overview of Bayesian Additive Regression Trees (BART). He also demonstrates how you can train and score BART models by using the new BART procedure and Bayesian Additive Regression Trees Action Set in SAS Visual Statistics.
A common task for statisticians and data scientists is to model an outcome variable (also called a target variable). You might use an outcome model for purely predictive purposes. Or use it as a tool for studying the relationship between the outcome and a particular predictor of interest. For either purpose, BART models have proven to be an effective, easy-to-use tool for outcome modeling.
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