使用CleanerR处理分类缺失数据

Anais do Brazilian e-Science Workshop (BreSci) Pub Date : 2019-06-24 DOI:10.5753/bresci.2019.6310

R. S. Pereira, F. Porto

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引用次数: 0

摘要

数据丢失是数据分析领域的一个常见问题。它们出现在数据集中的原因有很多，从数据集成到糟糕的数据输入。当面对这个问题时，分析师必须决定如何处理丢失的数据，因为从分析中放弃这些值并不总是可取的。在本文中，我们将讨论一种考虑信息论和功能依赖的方法，以最佳地输入缺失值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dealing with categorical missing data using CleanerR

Missing data is a common problem in the world of data analysis. They appear in datasets due to a multitude of reasons, from data integration to poor data input. When faced with the problem, the analyst must decide what to do with the missing data since its not always advisable to discard these values from your analysis. On this paper we shall discuss a method that takes into account information theory and functional dependencies to best imput missing values.

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Anais do Brazilian e-Science Workshop (BreSci)

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