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Building more accurate decision trees with the additive tree.

José Marcio Luna1, Efstathios D Gennatas2, Lyle H Ungar3

  • 1Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104; gilmer.valdes@ucsf.edu jose.luna@pennmedicine.upenn.edu jhf@stanford.edu.

Proceedings of the National Academy of Sciences of the United States of America
|September 19, 2019
PubMed
Summary

Interpretable machine learning models are crucial for high-stakes decisions. This study formalizes the additive tree, bridging interpretable Classification and Regression Trees (CART) and accurate gradient boosting, offering a spectrum of models for enhanced decision-making.

Keywords:
CARTadditive treedecision treegradient boostinginterpretable machine learning

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Interpretable machine learning is vital for high-stakes domains like medicine.
  • Classification and Regression Trees (CART) offer intuitive predictions but limited accuracy.
  • Ensemble methods like gradient boosting enhance accuracy but reduce model interpretability.

Purpose of the Study:

  • To bridge the gap between interpretable CART models and accurate ensemble methods.
  • To introduce a formalization of the additive tree learning technique.
  • To demonstrate the spectrum of models achievable with additive trees.

Main Methods:

  • Formalizing the additive tree as a learning technique.
  • Empirically validating the additive tree method.
  • Analyzing the spectrum of models from CART to gradient boosted stumps.

Main Results:

  • Additive trees can produce models equivalent to CART or gradient boosted stumps.
  • A single parameter variation controls the position on the CART-to-boosting spectrum.
  • Hybrid models derived from additive trees can outperform CART and boosted stumps.

Conclusions:

  • Additive trees offer a unified framework for interpretable and accurate decision tree models.
  • This approach provides a spectrum of models suitable for high-stakes applications.
  • Additive trees can achieve superior predictive performance through hybrid models.