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Random forest methodology for model-based recursive partitioning: the mobForest package for R.

Nikhil R Garge1, Georgiy Bobashev, Barry Eggleston

  • 1Health Sciences Division, Social, Statistical and Environmental Sciences, Research Triangle Institute, 3040 Cornwallis Road, Cox 342, Research Triangle Park, NC 27709, USA. ngarge@rti.org

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Summary
This summary is machine-generated.

This study introduces mobForest, an R package enhancing model-based recursive partitioning with random forests for more stable predictions. It addresses the sensitivity of single-tree models by aggregating predictions from multiple trees built on random data samples.

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Area of Science:

  • Statistics
  • Machine Learning
  • Computational Biology

Background:

  • Recursive partitioning is a non-parametric modeling technique for regression and classification.
  • Model-based recursive partitioning (mob) identifies observation groups with similar model parameters.
  • Existing mob methods using single trees are sensitive to learning sample variations.

Purpose of the Study:

  • To extend model-based recursive partitioning for improved prediction stability.
  • To introduce an R package implementing ensemble methods for mob.
  • To provide tools for assessing predictive accuracy and model performance.

Main Methods:

  • Implemented bagging and random forests for model-based recursive partitioning.
  • Developed the "mobForest" R package for constructing and aggregating multiple model-based trees.
  • Utilized bootstrapping and subsampling for generating random samples from learning data.

Main Results:

  • The mobForest package provides more stable predictions by aggregating results from numerous model-based trees.
  • The package includes functionalities for calculating predictive accuracy estimates and generating plots.
  • Offers tools for residual analysis and variable importance assessment.

Conclusions:

  • The mobForest package offers a random forest approach to model-based recursive partitioning.
  • This R package enhances prediction stability and model interpretability.
  • The mobForest package and its source code are publicly available.