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Forward variable selection for random forest models.

Jasper Velthoen1, Juan-Juan Cai2, Geurt Jongbloed1

  • 1Department of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.

Journal of Applied Statistics
|September 18, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a new interpretable prediction method using forward variable selection and the continuous ranked probability score (CRPS). The approach significantly reduces false positives in variable selection for high-dimensional data.

Area of Science:

  • Statistics
  • Machine Learning
  • Environmental Science

Background:

  • Random forests are effective for high-dimensional data but lack interpretability.
  • Interpretable predictive models are crucial for understanding complex relationships.

Purpose of the Study:

  • To develop a forward variable selection method for interpretable predictive modeling.
  • To minimize the continuous ranked probability score (CRPS) for optimal variable selection.
  • To provide a statistically rigorous method for selecting relevant covariates.

Main Methods:

  • A stepwise forward selection procedure minimizing CRPS.
  • A stopping criterion based on CRPS risk difference estimation.
  • Mathematical proof of optimality in a population sense.
Keywords:
CRPSRandom forestscorrelated covariatesforward selectionvariable selection

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  • Simulation studies comparing performance with existing methods.

    • Main Results:

    • The proposed method achieves a lower false positive rate compared to existing techniques.
    • Demonstrated effectiveness in statistical post-processing of temperature forecasts.
    • Selected approximately 10% of covariates while maintaining predictive power.

    Conclusions:

    • The developed method offers an interpretable alternative for high-dimensional prediction.
    • It provides a robust approach for variable selection in statistical modeling.
    • The method is applicable to real-world forecasting problems, enhancing model transparency.