Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing
复制标题
分类属性转换环境 (CANE)：用于分类数据预处理的 Python 模块
DOI：
10.1016/j.simpa.2022.100359
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
R. Mendes
中科院分区：
文献类型：
--
作者：
Luís Miguel Matos;J. Azevedo;Arthur Matta;A. Pilastri;Paulo Cortez;R. Mendes
分类属性转换环境（CANE）是一个简单但功能强大的数据分类预处理Python包。这个包是有价值的，因为目前有大量的机器学习（ML）算法只能使用数字数据进行训练（例如，深度学习，支持向量机），并且一些现实世界的ML应用程序与分类数据属性相关。目前，CANE提供了三种分类到数字的转换方法，即百分比分类修剪（PCP）、逆文档频率（IDF）和更简单的One-Hot-Encoding方法。此外，CANE模块有很好的文档，其中有几个代码示例，可以帮助非专业用户采用它。
Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package is valuable since there is currently a large range of Machine Learning (ML) algorithms that can only be trained using numerical data (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with categorical data attributes. Currently, CANE offers three categorical to numeric transformation methods, namely: Percentage Categorical Pruned (PCP), Inverse Document Frequency (IDF) and a simpler One-Hot-Encoding method. Additionally, the CANE module is well documented with several code examples that can help in its adoption by non expert users.