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treeheatr: an R package for interpretable decision tree visualizations.

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  • 1Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

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Summary
This summary is machine-generated.

The treeheatr R package offers interpretable decision tree visualizations, displaying data as heatmaps at leaf nodes. This tool enhances understanding of tree models and feature importance for both students and researchers.

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Area of Science:

  • Data Visualization
  • Machine Learning

Background:

  • Decision tree models are fundamental in machine learning.
  • Visualizing decision tree performance and data distribution is crucial for interpretation.
  • Existing visualization methods may not fully integrate tree structure with data characteristics.

Purpose of the Study:

  • To introduce treeheatr, an R package for creating interpretable decision tree visualizations.
  • To provide a tool that integrates tree structure with data heatmaps at leaf nodes.
  • To facilitate a deeper understanding of decision tree models and feature importance.

Main Methods:

  • Development of the treeheatr R package.
  • Implementation of heatmap visualizations at decision tree leaf nodes.
  • Integration of tree structure and data overview for enhanced interpretability.

Main Results:

  • treeheatr provides an integrated view of decision tree structure and data.
  • Visualizations reveal how feature splits partition the data space.
  • The package aids in uncovering data correlation structures and feature importance.

Conclusions:

  • treeheatr enhances the interpretability of decision tree models.
  • The package serves as a valuable educational tool for learning about decision trees.
  • It supports in-depth analysis of model performance and data characteristics.