论文标题
无分配预测推断的培训条件覆盖范围
Training-conditional coverage for distribution-free predictive inference
论文作者
论文摘要
无分布预测推理的领域为可证明有效的预测提供了工具，而无需对数据的分布进行任何假设，可以将其与任何回归算法配对，以提供准确可靠的预测间隔。这些方法提供的保证通常是边缘的，这意味着在训练数据集和查询的测试点中，预测精度平均保持。但是，最好获得更强大的训练条件覆盖范围的保证，这将确保大多数绘制训练数据集可以在未来的测试点上具有准确的预测准确性。已知该属性适用于分裂的保形预测方法。在这项工作中，我们研究了其他几种无分配预测推理方法的训练条件覆盖范围，并发现训练条件覆盖范围是通过某些方法实现的，但如果没有其他假设，则无法保证。
The field of distribution-free predictive inference provides tools for provably valid prediction without any assumptions on the distribution of the data, which can be paired with any regression algorithm to provide accurate and reliable predictive intervals. The guarantees provided by these methods are typically marginal, meaning that predictive accuracy holds on average over both the training data set and the test point that is queried. However, it may be preferable to obtain a stronger guarantee of training-conditional coverage, which would ensure that most draws of the training data set result in accurate predictive accuracy on future test points. This property is known to hold for the split conformal prediction method. In this work, we examine the training-conditional coverage properties of several other distribution-free predictive inference methods, and find that training-conditional coverage is achieved by some methods but is impossible to guarantee without further assumptions for others.
