TY - JOUR
T1 - Building more accurate decision trees with the additive tree
AU - Luna, José Marcio
AU - Gennatas, Efstathios D.
AU - Ungar, Lyle H.
AU - Eaton, Eric
AU - Diffenderfer, Eric S.
AU - Jensen, Shane T.
AU - Simone, Charles B.
AU - Friedman, Jerome H.
AU - Solberg, Timothy D.
AU - Valdes, Gilmer
N1 - Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Additive tree
KW - CART
KW - Decision tree
KW - Gradient boosting
KW - Interpretable machine learning
UR - http://www.scopus.com/inward/record.url?scp=85072786403&partnerID=8YFLogxK
U2 - 10.1073/pnas.1816748116
DO - 10.1073/pnas.1816748116
M3 - Article
C2 - 31527280
AN - SCOPUS:85072786403
SN - 0027-8424
VL - 116
SP - 19887
EP - 19893
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 40
ER -