论文标题
Problexity-用于二进制分类问题复杂性评估的开源Python库
problexity -- an open-source Python library for binary classification problem complexity assessment
论文作者
论文摘要
分类问题的复杂性评估是监督学习领域许多主题的重要因素。它在元学习中起着重要的作用 - 成为确定元属性或多准则优化的基础 - 允许评估训练集的重新采样，而无需重建识别模型。目前可用于学术界可用的工具，可以计算问题复杂性度量，仅作为C ++和R语言的库可用。本文介绍了软件模块，该模块允许估算Python语言的22种复杂性度量 - 与Scikit-Learn编程界面兼容 - 允许在机器学习社区最受欢迎的编程环境中使用它们实施研究。
The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning -- becoming the basis for determining meta-attributes or multi-criteria optimization -- allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language -- compatible with the scikit-learn programming interface -- allowing for the implementation of research using them in the most popular programming environment of the machine learning community.
