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Regression Trees With Fused Leaves.

Xiaogang Su1, Lei Liu2, Lili Liu2

  • 1Department of Mathematical Sciences, The University of Texas, El Paso, Texas, USA.

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Summary
This summary is machine-generated.

We introduce TreeFuL (Tree with Fused Leaves), a novel regression tree method. It creates more interpretable and accurate decision tree models for biomedical applications by fusing leaves and using cross-validation.

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CARTfused regularizationpruningregression treestree model selection

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Computational Biology

Background:

  • Traditional regression trees often require pruning to balance complexity and accuracy.
  • Existing methods may struggle with optimal node selection and model interpretability.

Purpose of the Study:

  • To introduce TreeFuL (Tree with Fused Leaves), a novel regression tree method.
  • To develop a more parsimonious and interpretable decision tree model without sacrificing predictive accuracy.

Main Methods:

  • TreeFuL combines recursive partitioning with fused regularization.
  • It employs cross-validated amalgamation of non-neighboring terminal nodes.
  • A leaf coloring scheme supports tree shearing and node amalgamation.

Main Results:

  • TreeFuL facilitates the development of more parsimonious tree models.
  • The method maintains predictive accuracy while enhancing interpretability.
  • Demonstrated advantages through simulation studies and an obesity dataset analysis.

Conclusions:

  • TreeFuL offers a distinct and effective alternative to conventional pruning methods.
  • The enhanced interpretability makes TreeFuL suitable for biomedical decision tree applications.
  • The method shows practical utility in disease diagnosis and prognosis prediction.