How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
arXiv:2606.03549v1 Announce Type: new Abstract: 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. To address this, we propose an integrated triplet-based plateau-search alg
The paper addresses a known difficulty in hyperparameter optimization for Random Forests, suggesting a refined approach to improve model efficiency and performance.
This research offers a methodical improvement in an established machine learning technique, potentially leading to more robust and less resource-intensive AI model development.
The proposed plateau search and Optuna integration provide a more efficient and less boundary-constrained method for tuning a critical hyperparameter in Random Forests.
- · Machine Learning Researchers
- · Data Scientists
- · AI Application Developers
- · Inefficient HPO methods
- · Projects constrained by computation limits
Random Forest models could become more reliably performant with lower optimization effort.
Improved efficiency in HPO could slightly reduce compute resource requirements for certain AI tasks.
The methodology might inspire similar HPO innovations for other ensemble or complex AI models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG