arXiv Statistics ML
Random Jump Forests
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Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
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How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary. Early-stopping strategies avoid fixing such a range, but can be sensitive to score noise and prone to premature stopping.