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Building more accurate decision trees with the additive tree.

Abstract：

: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.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Luna JM,Gennatas ED,Ungar LH,Eaton E,Diffenderfer ES,Jensen ST,Simone CB 2nd,Friedman JH,Solberg TD,Valdes G

doi

10.1073/pnas.1816748116

subject

Has Abstract

pub_date

2019-10-01 00:00:00

pages

19887-19893

issue

40

eissn

0027-8424

issn

1091-6490

pii

1816748116

journal_volume

116

pub_type

杂志文章

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