# On the Tunability of Random Survival Forests Model for Predictive Maintenance

arXiv: https://arxiv.org/abs/2504.14744 · HTML: https://mlsanity.com/paper/2504.14744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Hyperparameter Importance Across Datasets](https://mlsanity.com/paper/1710.04725/) (2017)
- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [The Role of Hyperparameters in Predictive Multiplicity](https://mlsanity.com/paper/2503.13506/) (2025)
- [Explainable Predictive Maintenance](https://mlsanity.com/paper/2306.05120/) (2023)
- [Rashomon perspective for measuring uncertainty in the survival predictive maintenance models](https://mlsanity.com/paper/2502.15772/) (2025)
- [Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications](https://mlsanity.com/paper/2504.12156/) (2025)

## Abstract (arXiv)

This paper investigates the tunability of the Random Survival Forest (RSF) model in predictive maintenance, where accurate time-to-failure estimation is crucial. Although RSF is widely used due to its flexibility and ability to handle censored data, its performance is sensitive to hyperparameter configurations. However, systematic evaluations of RSF tunability remain limited, especially in predictive maintenance contexts. We introduce a three-level framework to quantify tunability: (1) a model-level metric measuring overall performance gain from tuning, (2) a hyperparameter-level metric assessing individual contributions, and (3) identification of optimal tuning ranges. These metrics are evaluated across multiple datasets using survival-specific criteria: the C-index for discrimination and the Brier score for calibration. Experiments on four CMAPSS dataset subsets, simulating aircraft engine degradation, reveal that hyperparameter tuning consistently improves model performance. On average, the C-index increased by 0.0547, while the Brier score decreased by 0.0199. These gains were consistent across all subsets. Moreover, ntree and mtry showed the highest average tunability, while nodesize offered stable improvements within the range of 10 to 30. In contrast, splitrule demonstrated negative tunability on average, indicating that improper tuning may reduce model performance. Our findings emphasize the practical importance of hyperparameter tuning in survival models and provide actionable insights for optimizing RSF in real-world predictive maintenance applications.