4 papers
Mitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of Classifiers
Mustafa Cavus
As machine learning models are increasingly deployed in high-stakes environments, ensuring both probabilistic reliability and prediction stability has become critical. This paper e…
Rashomon perspective for measuring uncertainty in the survival predictive maintenance models
Yigitcan Yardimci, Mustafa Cavus
The prediction of the Remaining Useful Life of aircraft engines is a critical area in high-reliability sectors such as aerospace and defense. Early failure predictions help ensure…
On the Tunability of Random Survival Forests Model for Predictive Maintenance
Yigitcan Yardımcı, Mustafa Cavus
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 wi…
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications
Mustafa Cavus
In many applications, especially those involving prediction, models may yield near-optimal performance yet significantly disagree on individual-level outcomes. This phenomenon, kno…