DIGEN

DIGEN是由宾夕法尼亚大学医学信息研究所开发的合成数据集，包含40个数学函数生成的数据集，用于评估机器学习分类算法。每个数据集包含1000个样本，由10个标准正态分布特征生成。数据集旨在通过多样化的性能表现，揭示不同机器学习算法的优劣。该资源不仅提供数据集，还提供生成函数和详细的分析工具，支持机器学习社区进行算法评估和改进。

DIGEN is a synthetic dataset developed by the Institute of Medical Informatics, University of Pennsylvania. It consists of 40 datasets generated using mathematical functions, designed for evaluating machine learning classification algorithms. Each dataset contains 1,000 samples generated from 10 standard normally distributed features. This resource suite aims to uncover the strengths and weaknesses of various machine learning algorithms through diverse performance profiles. Furthermore, this resource not only provides the datasets themselves, but also their corresponding generation functions and comprehensive analytical tools, to support the machine learning community in algorithm evaluation and improvement.