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Boosted Model Tool

The Boosted Model tool provides generalized boosted regression models based on the gradient boosting methods of Friedman. This method is a modern statistical learning model that self-determines which subset of fields best predict a target field of interest, is able to capture highly nonlinear relationships and interactions between fields, and can automatically address a broad range of regression and classification problems in a way that can be transparent to the user.

The tool is applicable to a wide range of problems, including classification, count data, and continuous target regression problems and works by serially adding simple decision tree models to a model ensemble to minimize an appropriate loss function. See Gradient Boosting.

This tool uses the R programming language. Go to Options > Download Predictive Tools to install R and the packages used by the R Tool.