lucie:一个改进的Python包,用于从UCI机器学习库加载数据集
Kenneth Ge1, Phuc Nguyen2, Ramy Arnaout3
1Department of Pathology at Beth Israel Deaconess Medical Center (BIDMC), and is a student at Carnegie Mellon University.
bioRxiv : the preprint server for biology
|November 1, 2024
概括
一个名为lucie的新实用程序简化了从加利福尼亚大学-伊尔文 (UCI) 机器学习库中导入具有挑战性的数据集. 它成功地导入了95.4%以前无法访问的数据集,改进了现有的工具.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 加利福尼亚大学-伊尔文 (UCI) 机器学习存储库 (UCIMLR) 是对高影响数据集的重要资源.
- 很大一部分UCIMLR数据集,特别是那些具有非标准格式的zip文件中的数据集,很难使用推的ucimlrepo包来导入.
研究的目的:
- 开发一个自动化从UCIMLR进口以前无法进口的数据集的实用程序.
- 为了在进口过程中保留这些数据集的表格数据结构.
主要方法:
- 开发了一个新的Python包,lucie (加利福尼亚大学欧文大学加载例子).
- lucie自动确定数据格式,并促进进口.
- 该实用程序是使用UCIMLR前100个数据集设计的,并对随后的130个数据集进行了基准测试.
主要成果:
- lucie在导入具有挑战性的数据集方面取得了95.4%的成功率,而ucimlrepo.com的成功率为73.1%.
- 该包在以前无法通过自动化方法访问的数据集上表现出高性能.
- lucie提供98%的代码覆盖率,可在PyPI.
结论:
- 卢西显著提高了大量UCIMLR数据集的可访问性.
- 该实用程序解决了用于机器学习研究的数据导入的关键差距.
- lucie为数据科学家和研究人员提供了强大而高效的解决方案.
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