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Mixed-Effects Frequency-Adjusted Borders Ordinal Forest: A Tree Ensemble Method for Ordinal Prediction with
1Department of Statistics, TU Dortmund University, Dortmund, Germany.
Multivariate Behavioral Research
|September 9, 2025
Summary
A new machine learning method, Mixed-Effects Frequency-Adjusted Borders Ordinal Forest (mixfabOF), improves ordinal prediction for hierarchical data. It outperforms existing methods, especially when random effects have high variability.
Area of Science:
- Social and Life Sciences
- Statistical Modeling
- Machine Learning
Background:
- Ordinal prediction is crucial in social and life sciences for tasks like analyzing school grades or rating scales.
- Existing machine learning (ML) methods, like Random Forest (RF), show high predictive power but often lack support for hierarchical data structures.
- Hierarchical data, common in these fields (e.g., students within classes), requires specialized methods for accurate prediction.
Purpose of the Study:
- To extend the Frequency-Adjusted Borders Ordinal Forest (fabOF) machine learning method to accommodate hierarchical data.
- To introduce Mixed-Effects Frequency-Adjusted Borders Ordinal Forest (mixfabOF) for improved ordinal prediction in nested data settings.
- To evaluate the performance of mixfabOF against existing RF-based ordinal prediction methods.
Main Methods:
- Development of Mixed-Effects Frequency-Adjusted Borders Ordinal Forest (mixfabOF) by extending the fabOF method.
- Utilizing an iterative expectation-maximization-type estimation procedure within the mixfabOF framework.
- Comparative analysis of mixfabOF against fabOF and other RF-based ordinal prediction techniques on hierarchical datasets.
Main Results:
- mixfabOF demonstrated superior performance compared to fabOF and other RF-based methods in hierarchical data settings with high random effect variability.
- For settings with lower random effect variability, mixfabOF achieved performance comparable to fabOF and alternative RF-based methods.
- The proposed method effectively handles the complexities of nested data structures in ordinal prediction tasks.
Conclusions:
- Mixed-Effects Frequency-Adjusted Borders Ordinal Forest (mixfabOF) offers a significant advancement for ordinal prediction in hierarchical data.
- The method provides a valuable tool for researchers in the social and life sciences dealing with nested data structures.
- mixfabOF enhances predictive accuracy, particularly in scenarios characterized by substantial random effect variation.
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