This guide provides an introduction into RandomForests Modeling Basics.

This guide describes what’s under the hood, beginning with why RandomForests’ engine is both unique and innovative. Because RandomForests is such a new tool, we assume no prior knowledge of the adaptive modeling methodology underlying RandomForests. To put this methodology into context, the first section discusses the modeler’s challenge and addresses how RandomForests meets this challenge. The remaining sections provide detailed explanations of how the RandomForests model is generated, how RandomForests handles categorical variables and missing values, how the “optimal” model is selected and, finally, how testing regimens are used to protect against overfitting.

[J#426:1709]