The quality of data in training the machine learning models is one the most important factor while developing such models. The quality means here is the accuracy and consistency of labeled data. While calculating the training data, benchmarks consensus, and review are the industry standards followed by annotators. We need to figure out here what combination of these quality assurance procedures is suitable for your project.
Here in this article you will learn about the definitions of quality, consistency and accuracy and why quality matters in training the machine
learning models. Here you will also get to know about the industry standard methods to quantify quality and what are the most cutting-edge tools used to automate quality assurance processes in this field.
Consistency or Accuracy, which one is important
Quality here is directly related to consistency and accuracy, this is not just how correct a data or label is but...