ML teams use error analysis to improve model performance. By systematically identifying mispredictions - where model predictions disagree with ground truth labels - ML teams can deliver targeted improvements to their training data and boost model metrics.
Mispredictions are usually due to a model error (poor model prediction), or a labeling mistake (ground truth is wrong), or both. Labelbox helps ML teams identify mispredictions, bucket them into patterns of failures, and fix the highest-impact patterns of failures.
Here are some ways Labelbox helps fix high-impact model errors and label errors:
- With Labelbox, surface model errors, find data that is similar to model errors, label it, and re-train the model on the new labels, to fix the failure mode
- With Labelbox, surface label errors, correct the labeling mistake, update model metrics, and re-train the model on the updated labels, to fix the failure mode
In this section, we detail workflows to:
Once you have delivered a targeted improvement to your training data and re-trained your models, you can compare model performance.
Not all data is created equal. A crucial question for ML teams is: among all my unlabeled data, what should I label in priority?
Active learning is the art and science of identifying what data will most dramatically improve model performance and feeding that insight into the prioritization of data for labeling.
By focusing data labeling and data debugging efforts on the data that will most dramatically improve model performance, machine learning teams can save time and resources.
Labelbox helps ML teams identify and prioritize high-value data to label.
Updated 3 months ago