论文标题
Mapie：用于无分配不确定性量化的开源库
MAPIE: an open-source library for distribution-free uncertainty quantification
论文作者
论文摘要
估计与机器学习预测（ML）模型相关的不确定性对于评估其稳健性和预测能力至关重要。在此提交中，我们介绍了Mapie（模型不可知的预测间隔估计器），这是一个开源Python库，可量化用于单输出回归和多类分类任务的ML模型的不确定性。 Mapie实施了保形预测方法，使用户可以轻松地计算出在边际覆盖范围内具有强大理论保证的不确定性，并在模型或基础数据分布上进行了轻微的假设。 Mapie托管在Scikit-Learn-Contrib上，完全“ Scikit-Learn兼容”。因此，它接受带有Scikit-Learn API的任何类型的回归器或分类器。该库可用：https：//github.com/scikit-learn-contrib/mapie/。
Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power. In this submission, we introduce MAPIE (Model Agnostic Prediction Interval Estimator), an open-source Python library that quantifies the uncertainties of ML models for single-output regression and multi-class classification tasks. MAPIE implements conformal prediction methods, allowing the user to easily compute uncertainties with strong theoretical guarantees on the marginal coverages and with mild assumptions on the model or on the underlying data distribution. MAPIE is hosted on scikit-learn-contrib and is fully "scikit-learn-compatible". As such, it accepts any type of regressor or classifier coming with a scikit-learn API. The library is available at: https://github.com/scikit-learn-contrib/MAPIE/.
