Conformal prediction uses past experience to determine precise levels of
confidence in new predictions. Given an error probability $\epsilon$, together
with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a
set of labels, typically containing $\hat{y}$, that also contains $y$ with
probability $1-\epsilon$. Conformal prediction can be applied to any method for
producing $\hat{y}$: a nearest-neighbor method, a support-vector machine, ridge
regression, etc.
Conformal prediction is designed for an on-line setting in which labels are
predicted successively, each one being revealed before the next is predicted.
The most novel and valuable feature of conformal prediction is that if the
successive examples are sampled independently from the same distribution, then
the successive predictions will be right $1-\epsilon$ of the time, even though
they are based on an accumulating dataset rather than on independent datasets.
In addition to the model under which successive examples are sampled
independently, other on-line compression models can also use conformal
prediction. The widely used Gaussian linear model is one of these.
This tutorial presents a self-contained account of the theory of conformal
prediction and works through several numerical examples. A more comprehensive
treatment of the topic is provided in "Algorithmic Learning in a Random World",
by Vladimir Vovk, Alex Gammerman, and Glenn Shafer (Springer, 2005).

该论文介绍了通过使用过去的经验来确定新预测的置信水平的改造预测方法，该方法可以应用于任何生成标签预测的方法，并且包括一些数值示例。