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Related Experiment Video

Updated: May 12, 2026

A Simple Planting Technique for Re-establishing Trees Where Frequent Inundation Occurs
04:41

A Simple Planting Technique for Re-establishing Trees Where Frequent Inundation Occurs

Published on: January 26, 2018

Super greedy trees.

Hemant Ishwaran1

  • 1Division of Biostatistics, Miller School of Medicine, University of Miami, Miami, USA.

Artificial Intelligence Review
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Super Greedy Trees (SGTs) offer a new decision-tree method using penalized models for complex data. Super Greedy Forests (SGFs) show strong performance in regression and identify key survival signals in clinical data.

Keywords:
Empirical riskLassoMultivariate cutsParametric modelsPartitions

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Quantifying Corticolous Arthropods Using Sticky Traps
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Last Updated: May 12, 2026

A Simple Planting Technique for Re-establishing Trees Where Frequent Inundation Occurs
04:41

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Published on: January 26, 2018

Quantifying Corticolous Arthropods Using Sticky Traps
05:28

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Published on: January 19, 2020

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Mining

Background:

  • Decision trees like CART are widely used but can struggle with complex data structures.
  • Existing methods may lack flexibility in partitioning data or interpretability.

Purpose of the Study:

  • Introduce Super Greedy Trees (SGTs), an extension of CART.
  • Develop a flexible and theoretically sound tree-based learning framework.
  • Enhance predictive accuracy and interpretability for complex regression tasks.

Main Methods:

  • SGTs construct tree splits using lasso-penalized parametric models at each node.
  • Adaptive multivariate geometric cuts (linear or curved) are used to reduce empirical risk.
  • An ensemble extension, Super Greedy Forests (SGFs), is also developed.

Main Results:

  • SGTs and SGFs demonstrate strong performance in simulated and real-world regression studies.
  • They outperform CART, oblique trees, random forests, and gradient boosted trees on complex response surfaces.
  • SGFs successfully identified sparse signal combinations linked to long-term survival in an ECG case study.

Conclusions:

  • The SGT framework provides a flexible and theoretically sound approach to tree-based learning.
  • SGTs and SGFs offer richer data partitions than traditional CART while maintaining interpretability.
  • This method is particularly effective for complex regression problems and identifying key predictive factors.