In machine learning, the quality of the data used to train your model directly affects its performance.
To get high-performance models, you need to ensure that the training data is relevant, consistent and free of errors. The keys to achieving high data quality are to be aware of the common quality errors, implement processes to surface those errors and iterate to fix them.
What may go wrong during data labeling differs from project to project. However, knowing these pitfalls and making a conscious effort to avoid them can help improve data quality so that your data is usable.
Misinterpretation of instruction
Instructions not written with clear and direct language may cause annotators to misinterpret what and how to annotate your data. Creating crystal clear instructions typically requires multiple iterations. We’ve shared our tips on writing good instructions here.
Edge and corner cases
Edge and corner cases are examples of domain variability in the real world that happen at the long-tail. Edge cases show up when you least expect it because they are rare, and may not be covered in your instructions to be labeled accurately.
Not a good fit between use case and tool
There is no ultimate labeling tool that solves every use case in the world. Sometimes poor label quality can result from a tool being unable to deliver the precision required. It is always good practice to run a test batch when evaluating a new tool or solution provider.
The demographic of annotators may introduce bias in the labeled data. This could be based on their education, background, and experience with the domain. Having a diverse and well-trained workforce can help reduce this bias.
Post-labeling, you might find that your dataset does not have the correct distribution of classes, which can reduce your model performance.
Now, even with an understanding of the common errors, your data might still face quality issues without a systematic approach to surface those errors. Below are a few methods used in the industry to bring to light label noise:
With the insights gathered from conducting the quality assessments above, there are multiple actions you can take to improve the quality of your labeled data in subsequent iterations.
With multiple factors affecting data labeling quality, it can be a manual and tedious chore to thoroughly review your labeled data (especially if it’s a large dataset!) to discover actionable insights for quality improvement.
We highlighted a few common data labeling errors and methods for surfacing label noise so that you can iterate your labeling instructions for better quality.
Improving data labeling quality requires feedback and multiple iterations. This is why we built SUPABOLT – a data labeling platform that makes this iterative process easier and faster for you with our Quality Insights tools.
Start a test project for free today and discover new ways of improving your labeled data quality.