Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn from data. In recent years, conformal prediction has emerged as a rapidly growing alternative framework that is particularly well suited to modern applications involving high-dimensional data and complex machine learning models. Its appeal stems from being both distribution-free -- relying mainly on symmetry assumptions such as exchangeability -- and model-agnostic, treating the learning algorithm as a black box. Even under such limited assumptions, conformal prediction provides exact finite-sample guarantees, though these are typically of a marginal nature that requires careful interpretation. This paper explains the core ideas of conformal prediction and reviews selected methods. Rather than offering an exhaustive survey, it aims to provide a clear conceptual entry point and a pedagogical overview of the field.

翻译：预测推断是统计学中的一项基本任务，传统上依赖于关于数据分布的参数化假设以及对模型如何从数据中学习的详细分析。近年来，共形预测作为一种快速发展的替代框架出现，特别适用于涉及高维数据和复杂机器学习模型的现代应用。其吸引力源于其兼具分布自由性——主要依赖可交换性等对称性假设——以及模型无关性，将学习算法视为黑箱。即使在如此有限的假设下，共形预测也能提供精确的有限样本保证，尽管这些保证通常具有边际性质，需要谨慎解释。本文解释了共形预测的核心思想，并综述了选定的方法。本文并非旨在提供详尽综述，而是力图提供该领域清晰的概念入门和教学概述。