A Guide to Data Labeling Quality Assurance in Machine Learning

The performance of a machine learning model is dependent on the quality of the training data. The consistency and correctness of labelled data in machine learning are used to assess quality. Benchmarks consensus, review, Cronbach's alpha test are some the industry standard procedures for calculating training data quality. One of the most important aspects of your work is determining which mix of these quality assurance processes is best for your project. Many data scientists and researchers tend to agree on a few characteristics of high-quality training datasets that they use in big data initiatives.