使用Automunge的数字编码选项
Numeric Encoding Options with Automunge
摘要：
使用表格数据的机器学习的主流实践可能会理所当然地认为，在深度神经网络的上下文中，任何超出数值集缩放的特征工程都是多余的。本文将通过调查表格数据管道的Automunge开源python库平台中可用的数字转换选项，为深度学习中数字流的扩展编码的潜在好处提供论据，其中转换可以应用于“家谱”集合中的不同列，具有派生的代和分支。Automunge变换选项包括归一化、合并、噪声注入、导数等。这些方法的聚合成家庭树集的变换被证明用于呈现数字特征，以机器学习在多种配置的不同的信息内容，可以应用于编码未知的解释的数字集。实验表明，实现了一种新的广义解决方案，数据增强的噪声注入表格学习，可能会大大有利于模型性能不足的训练数据的应用程序。
英文摘要：
Mainstream practice in machine learning with tabular data may take for granted that any feature engineering beyond scaling for numeric sets is superfluous in context of deep neural networks. This paper will offer arguments for potential benefits of extended encodings of numeric streams in deep learning by way of a survey of options for numeric transformations as available in the Automunge open source python library platform for tabular data pipelines, where transformations may be applied to distinct columns in "family tree" sets with generations and branches of derivations. Automunge transformation options include normalization, binning, noise injection, derivatives, and more. The aggregation of these methods into family tree sets of transformations are demonstrated for use to present numeric features to machine learning in multiple configurations of varying information content, as may be applied to encode numeric sets of unknown interpretation. Experiments demonstrate the realization of a novel generalized solution to data augmentation by noise injection for tabular learning, as may materially benefit model performance in applications with underserved training data.
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。
