SurvSet：一个开源的事件时间数据集存储库
SurvSet: An open-source time-to-event dataset repository
摘要：
事件时间（T2E）分析是统计学的一个分支，它对事件发生所需的持续时间进行建模。这些事件可能包括死亡、失业或产品失败等结果。大多数现代机器学习（ML）算法，如决策树和核方法，都支持使用数据科学软件（Python和R）进行T2E建模。为了补充这些开发，SurvSet是第一个开源T2E数据集存储库，旨在对ML算法和统计方法进行快速基准测试。SurvSet中的数据格式一致，因此单一预处理方法适用于所有数据集。SurvSet目前有76个数据集，这些数据集在维度、时间依赖性和背景方面各不相同（其中大部分来自生物医学）。SurvSet在PyPI上可用，可以通过pip install SurvSet安装。R用户可以直接从相应的git仓库下载数据。
英文摘要：
Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, like decision trees and kernel methods, are supported for T2E modelling with data science software (python and R). To complement these developments, SurvSet is the first open-source T2E dataset repository designed for a rapid benchmarking of ML algorithms and statistical methods. The data in SurvSet have been consistently formatted so that a single preprocessing method will work for all datasets. SurvSet currently has 76 datasets which vary in dimensionality, time dependency, and background (the majority of which come from biomedicine). SurvSet is available on PyPI and can be installed with pip install SurvSet. R users can download the data directly from the corresponding git repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
