A Stationary-Distribution Theory for Triplet-Based Plateau Search in Random Forest Ensemble-Size Selection
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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…
1Key Takeaways
- 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….
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that 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.
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