arXiv:2507.14023v2 Announce Type: replace-cross Abstract: Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new covariate value. Most of the existing literature has focused either on point prediction or on interval prediction based on asymptotic approximations. We develop conformal prediction intervals for bounded outcomes within the framework of transformation regression models, encompassing widely used models
The continuous development in machine learning methods, driven by increased computational power and data availability, necessitates more robust uncertainty quantification techniques for real-world applications.
Improved conformal prediction methods for bounded outcomes enhance the reliability and interpretability of AI models in critical applications, reducing risk and increasing trust in automated decision-making.
The ability to generate more accurate and reliable prediction intervals for bounded continuous variables will lead to more trustworthy and deployable AI systems in fields like healthcare, finance, and industrial control.
- · Machine Learning Researchers
- · Data Scientists
- · Healthcare sector
- · Financial Services
- · Developers of less robust statistical methods
- · Industries reliant on black-box AI predictions
The new method provides more reliable uncertainty estimates for AI predictions in practical applications such as medical diagnosis or credit scoring.
Increased confidence in AI model outputs could accelerate adoption in highly regulated industries, leading to greater automation and efficiency.
Standardization of conformal prediction techniques might emerge, becoming a baseline requirement for regulatory approval of AI systems in critical infrastructure.
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Read at arXiv cs.LG