Guide to Measuring Quality

Measuring The Quality Of Labeled Data

Accuracy, Recall, Precision, And F1 Score

Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML use case. Your requirements will be driven by the use case, and you will need to evaluate the quality of your data annotation over multiple dimensions, including completeness, exactness, and accuracy. But before you can measure quality, you need to establish an unambiguous set of rules that describe what “quality” means in the context of your project.

Alegion's Data Science Team put together a whitepaper on annotation guidelines and improving the quality of labeling data.

Download a complimentary copy of this whitepaper to upgrade your quality.