Download the white paper: Big Data Decision Trees with R

by Richard Calaway, Lee Edlefsen, and Lixin Gong, Revolution Analytics

Revolution Analytics’ RevoScaleR package provides full-featured, fast, scalable, distributable predictive data analytics.  The included rxDTree function provides the ability to estimate decision trees efficiently on very large data sets. Decision trees (Breiman, Friedman, Olshen, & Stone, 1984) provide relatively easy-to-interpret models, and are widely used in a variety of disciplines.  For example,

  • Predicting which patient characteristics are associated with high risk of, for example, heart attack.
  • Deciding whether or not to offer a loan to an individual based on individual characteristics.
  • Predicting the rate of return of various investment strategies

The rxDTree function fits tree models using a binning-based recursive partitioning algorithm. The resulting model is similar to that produced by the recommended R package rpart (Therneau & Atkinson, 1997). Both classification-type trees and regression-type trees are supported.

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