Data Quality Evaluation of Numeric Data for Classification Tasks

Overview & Synopsis

Data quality has become a focus in a wide array of industrial and research applications. This is especially true for machine learning, as most models rely heavily on data. However, assessing data quality before and after repairing is not an easy task.Indeed, data quality is usually assessed through data dimensions that describe data;however, there are no unified definitions for most of these dimensions and no unifiedmetric for data quality as a whole.First, this thesis investigates the question: ”Is it always better to repair data?”by studying the impact of data deterioration on classification performances beforeand after repairing data. A method to decompose repairing approaches paired witha metric to evaluate how difficult it is to use different data repairing methods is alsoproposed.Second, this thesis presents a unified metric to measure data quality for classification tasks. This metric is designed such that no reference data or expertiseis needed. It is based upon two main terms: the former evaluates classificationperformance across a wide range of models, and the latter assesses variations ofperformance when a low amount of errors is injected into datasets.Finally, this thesis proposes a metric to measure the degree of data repairabilitycombined with a metric of the difficulty of using a repairing pipeline.