On some practical challenges of conformal prediction
This work addresses incremental improvements for data scientists using conformal prediction to enhance reliability and efficiency in real-world applications.
The paper tackles practical challenges in conformal prediction, such as approximate region determination, high computational cost, and uncontrolled region shapes, by proposing a simple strategy based on new insights into monotonicity relationships to address these issues.
Conformal prediction is a model-free machine learning method for creating prediction regions with a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity of prediction, (ii) the computation required could be prohibitively expensive, and (iii) the shape of a conformal prediction region is hard to control. This article offers new insights into the relationship among the monotonicity of the non-conformity measure, the monotonicity of the plausibility function, and the exact determination of a conformal prediction region. Based on these new insights, we propose a simple strategy to alleviate the three challenges simultaneously.