Skip to main content
Open Access Publications from the University of California


UC San Francisco Previously Published Works bannerUCSF

Building more accurate decision trees with the additive tree.

  • Author(s): Luna, José Marcio
  • Gennatas, Efstathios D
  • Ungar, Lyle H
  • Eaton, Eric
  • Diffenderfer, Eric S
  • Jensen, Shane T
  • Simone, Charles B
  • Friedman, Jerome H
  • Solberg, Timothy D
  • Valdes, Gilmer
  • et al.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
Current View