自动分类编码的解析
Parsed Categoric Encodings with Automunge
摘要：
用于表格数据预处理的Automunge开源python库平台自动化数字编码和缺失数据填充的特征工程数据转换，以在适合指定训练集中的列的属性的基础上接收整洁的数据，以用于一致且有效地应用于后续数据管道，例如用于推理，其中变换可以应用于具有派生的世代和分支的“家谱”集合中的不同列。包括在转换库中的是通过自动字符串解析从有界分类字符串集合中提取结构的方法，其中解析唯一值集合中的条目之间的比较以识别字符子集重叠，所述字符子集重叠可以通过布尔重叠检测激活的附加列或通过用识别的重叠分区替换字符串条目来编码。也可以应用于无界分类集合的另外的字符串解析选项包括从条目或搜索函数中提取数字子串分区以识别指定子串分区的存在。这些方法聚集成变换的“家谱”集合被证明用于从与列中的条目集合相关的分类串组合自动提取结构，诸如可以应用于准备用于机器学习的分类串集合编码而无需人工干预。
英文摘要：
The Automunge open source python library platform for tabular data pre-processing automates feature engineering data transformations of numerical encoding and missing data infill to received tidy data on bases fit to properties of columns in a designated train set for consistent and efficient application to subsequent data pipelines such as for inference, where transformations may be applied to distinct columns in "family tree" sets with generations and branches of derivations. Included in the library of transformations are methods to extract structure from bounded categorical string sets by way of automated string parsing, in which comparisons between entries in the set of unique values are parsed to identify character subset overlaps which may be encoded by appended columns of boolean overlap detection activations or by replacing string entries with identified overlap partitions. Further string parsing options, which may also be applied to unbounded categoric sets, include extraction of numeric substring partitions from entries or search functions to identify presence of specified substring partitions. The aggregation of these methods into "family tree" sets of transformations are demonstrated for use to automatically extract structure from categoric string compositions in relation to the set of entries in a column, such as may be applied to prepare categoric string set encodings for machine learning without human intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
