The superiority of conformal prediction in uncertainty quantification
Quantifying uncertainty is fundamental to building robust and reliable machine learning models. Several methodologies have emerged over the years, each with its own merits. However, conformal prediction stands out as a particularly compelling framework. Let’s explain why:
- Distribution-free framework: One of the most notable features of conformal prediction is that it doesn’t make any assumptions about the distribution of the data. Many uncertainty quantification methods are based on certain probabilistic assumptions or rely on specific data distributions to function effectively. In contrast, conformal prediction remains agnostic to these considerations, making it versatile and widely applicable across diverse datasets.
- Theoretical guarantees: conformal prediction offers robust theoretical guarantees for its predictions. Specifically, it provides a set of potential outcomes for a prediction...
