A Stationary-Distribution Theory for Triplet-Based Plateau Search in Random Forest Ensemble-Size Selection
arXiv:2606.30837v1 Announce Type: new Abstract: The number of trees is a central computational parameter in Random Forests: increasing it reduces finite-ensemble variability but increases training and prediction cost. Plateau-based tuning adapts this parameter through local comparisons of out-of-bag scores at a geometric triplet of tree counts. After the remaining hyperparameters have stabilized, however, the central triplet point need not converge to a deterministic value; instead, it fluctuates around a stationary regime. This paper develops a stationary-distribution theory for this process.
The continuous growth in machine learning model complexity necessitates more refined and efficient methods for hyperparameter tuning, especially for foundational algorithms like Random Forests.
Optimizing computational parameters in ensemble models directly impacts the efficiency and cost of training and deploying AI systems, a critical factor as AI scales.
This research provides a theoretical underpinning for a more robust method of selecting the optimal number of trees in Random Forests, potentially leading to more stable and efficient model deployment.
- · Machine Learning Researchers
- · AI/ML Developers
- · Industries using Random Forests
- · Inefficient ML training processes
More precise and computationally efficient Random Forest models will be developed and deployed.
Reduced computational overhead for certain AI applications could free up compute resources for other tasks.
The theoretical framework might be extended to other ensemble methods, leading to broader optimization advancements in AI.
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