Inductive conformal predictors (ICPs) are algorithms that are able to
generate prediction sets, instead of point predictions, which are valid at a
user-defined confidence level, only assuming exchangeability. These algorithms
are useful for reliable machine learning and are increasing in popularity. The
ICP development process involves dividing development data into three parts:
training, calibration and test. With access to limited or expensive development
data, it is an open question regarding the most efficient way to divide the
data. This study provides several experiments to explore this question and
consider the case for allowing overlap of examples between training and
calibration sets. Conclusions are drawn that will be of value to academics and
practitioners planning to use ICPs.

本研究通过多个实验证明了在有限或昂贵的开发数据情况下，允许训练集和校准集之间的示例重叠是一种高效的方式，从而提供了关于使用 ICPs 的价值的结论。

研究感应阵预测模型中的数据使用

Investigating Data Usage for Inductive Conformal Predictors

Conformal predictors are set predictors that are automatically valid in the
sense of having coverage probability equal to or exceeding a given confidence
level. Inductive conformal predictors are a computationally efficient version
of conformal predictors satisfying the same property of validity. However,
inductive conformal predictors have been only known to control unconditional
coverage probability. This paper explores various versions of conditional
validity and various ways to achieve them using inductive conformal predictors
and their modifications.

本文探讨了使用归纳一致性预测器及其修改版本实现有条件有效性的各种方法，以及这些方法在覆盖率概率和计算效率方面的表现。