训练数据的质量对机器学习任务的效率、准确性和复杂性有着巨大的影响。有各种工具和技术可用于评估与一般清理和分析检查有关的数据质量。然而，这些技术不适用于检测数据问题的背景下，机器学习任务，如嘈杂的标签，存在重叠的类等，我们试图重新审视数据质量问题的背景下，建立一个机器学习管道，并建立一个工具，可以检测，解释和补救问题的数据，并系统和自动捕获所有的变化应用到数据。我们引入了用于机器学习的数据质量工具包，作为一些关键质量指标和相关补救技术的库，以分析和增强机器学习项目的结构化训练数据集的准备程度。该工具包可以减少数据准备管道的周转时间，并简化数据质量评估过程。我们的工具包通过IBM API Hub [1]平台公开提供，任何开发人员都可以使用IBM的Data Quality for AI API [2]评估数据质量。详细的教程也可以在IBM Learning Path [3]上找到。
英文摘要：
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Various tools and techniques are available that assess data quality with respect to general cleaning and profiling checks. However these techniques are not applicable to detect data issues in the context of machine learning tasks, like noisy labels, existence of overlapping classes etc. We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data. We introduce the Data Quality Toolkit for machine learning as a library of some key quality metrics and relevant remediation techniques to analyze and enhance the readiness of structured training datasets for machine learning projects. The toolkit can reduce the turn-around times of data preparation pipelines and streamline the data quality assessment process. Our toolkit is publicly available via IBM API Hub [1] platform, any developer can assess the data quality using the IBM's Data Quality for AI apis [2]. Detailed tutorials are also available on IBM Learning Path [3].
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University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）
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