Finding Optimal Normalizing Transformations via bestNormalize
Finding Optimal Normalizing Transformations via bestNormalize
复制标题
通过 bestNormalize 寻找最佳标准化变换
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Ryan A. Peterson
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文献类型：
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作者：
Ryan A. Peterson
bestNormalize R包旨在帮助用户找到一种转换，可以有效地规范化向量，而不管其实际分布如何。已经开发的许多标准化技术中的每一种都有自己的优点和缺点，并且在完全观察到数据之前决定使用哪一种是困难的或不可能的。此包便于在一系列可能的转换之间进行选择，并将自动返回最佳转换，即，使数据看起来最正常的方法。为了评估和比较一系列可能的变换的归一化功效，我们开发了一种基于拟合优度检验除以其自由度的统计量。转换可以无缝地训练并应用于新观察到的数据，并且可以与机器学习工作流中的数据预处理的插入符号和配方结合实施。支持自定义转换和规范化统计信息。
The bestNormalize R package was designed to help users find a transformation that can effectively normalize a vector regardless of its actual distribution. Each of the many normalization techniques that have been developed has its own strengths and weaknesses, and deciding which to use until data are fully observed is difficult or impossible. This package facilitates choosing between a range of possible transformations and will automatically return the best one, i.e., the one that makes data look the most normal. To evaluate and compare the normalization efficacy across a suite of possible transformations, we developed a statistic based on a goodness of fit test divided by its degrees of freedom. Transformations can be seamlessly trained and applied to newly observed data, and can be implemented in conjunction with caret and recipes for data preprocessing in machine learning workflows. Custom transformations and normalization statistics are supported.