Related Experiment Video
Updated: Sep 22, 2025
12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.6K
Provable Boolean interaction recovery from tree ensemble obtained via random forests
Merle Behr1, Yu Wang1, Xiao Li1
1Department of Statistics, University of California, Berkeley, CA 94720.
Summary
Iterative Random Forests (iRFs) can discover Boolean biological interactions. A new model and method, LSSFind, theoretically guarantees consistent discovery of these feature interactions.
Area of Science:
- Genomics
- Machine Learning
- Computational Biology
Background:
- Random Forests (RFs) excel in prediction, particularly in genomics.
- Iterative RFs (iRFs) show promise for discovering Boolean biological interactions, crucial for functional genomics and precision medicine.
- Theoretical understanding of how tree-based methods find Boolean interactions is lacking.
Purpose of the Study:
- Introduce a novel discontinuous nonlinear regression model (Locally Spiky Sparse - LSS) inspired by biological thresholding.
- Define Depth-Weighted Prevalence (DWP) to quantify feature co-occurrence in RF ensembles.
- Establish a theoretically sound method (LSSFind) for consistent Boolean interaction discovery.
Main Methods:
- Developed the Locally Spiky Sparse (LSS) regression model.
- Defined Depth-Weighted Prevalence (DWP) for feature sets in RF ensembles.
- Proposed LSSFind, a tractable iRF procedure for interaction discovery.
Main Results:
- Proved that DWP attains a universal upper bound for Boolean interactions under the LSS model.
- Demonstrated that LSSFind consistently discovers interactions as sample size increases.
- Simulation results confirm LSSFind's ability to recover interactions, even with violated assumptions.
Conclusions:
- The LSS model provides a theoretical framework for understanding Boolean interactions in RFs.
- LSSFind offers a statistically grounded approach to identifying critical biological feature interactions.
- This work advances functional genomics and precision medicine through improved interaction discovery.
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