Derives the stationary distribution and asymptotic scaling O(ε^{-2}) for ensemble size in a Markov chain model of triplet-based plateau tuning for random forests.
Random Forests: some methodological insights
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both standard problems as well as high dimensional ones for which the number of variables hugely exceeds the sample size. But the main contribution of this paper is twofold: to provide some insights about the behavior of the variable importance index based on random forests and in addition, to propose to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The strategy involves a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.
fields
cs.LG 2years
2026 2representative citing papers
A triplet-based plateau search algorithm is proposed to adaptively determine a near-minimal number of trees for random forests by monitoring relative OOB score changes across forest size triplets, removing n_trees from the TPE search space.
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A Stationary-Distribution Theory for Triplet-Based Plateau Search in Random Forest Ensemble-Size Selection
Derives the stationary distribution and asymptotic scaling O(ε^{-2}) for ensemble size in a Markov chain model of triplet-based plateau tuning for random forests.
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How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
A triplet-based plateau search algorithm is proposed to adaptively determine a near-minimal number of trees for random forests by monitoring relative OOB score changes across forest size triplets, removing n_trees from the TPE search space.