论文标题
归纳性共形预测：一个简单的介绍，python中的示例
Inductive Conformal Prediction: A Straightforward Introduction with Examples in Python
论文作者
论文摘要
归纳性共形预测（ICP）是一组无分布和模型的不可分割算法，该算法旨在以用户定义的置信度预测，并具有覆盖范围保证。与其具有重点预测，即在回归的情况下或多类分类中的单个类中的实际数字，而是使用ICP输出校准的模型分别是一个间隔或一组类。 ICP在高风险设置中特别重要的是，我们希望真正的输出属于具有高概率的预测集。例如，分类模型可能会输出给定磁共振图像患者没有潜在疾病的磁共振图像。但是，该模型输出基于最可能的类别，第二个最有可能的类可能表明患者患有15％的脑瘤或其他严重疾病的机会，因此应进行进一步的检查。因此，使用ICP更具信息性，我们认为这应该是产生预测的标准方式。本文是动手介绍，这意味着我们将在介绍理论时提供示例。
Inductive Conformal Prediction (ICP) is a set of distribution-free and model agnostic algorithms devised to predict with a user-defined confidence with coverage guarantee. Instead of having point predictions, i.e., a real number in the case of regression or a single class in multi class classification, models calibrated using ICP output an interval or a set of classes, respectively. ICP takes special importance in high-risk settings where we want the true output to belong to the prediction set with high probability. As an example, a classification model might output that given a magnetic resonance image a patient has no latent diseases to report. However, this model output was based on the most likely class, the second most likely class might tell that the patient has a 15% chance of brain tumor or other severe disease and therefore further exams should be conducted. Using ICP is therefore way more informative and we believe that should be the standard way of producing forecasts. This paper is a hands-on introduction, this means that we will provide examples as we introduce the theory.
