Data Labelling: The Foundation of Supervised Machine Learning

Data labeling quality is crucial for model accuracy, with inter-annotator agreement and label error rate being key metrics. For most NLP tasks, aim for a Cohen’s Kappa of at least 0.70, while safety-critical CV tasks require 0.80. Ensuring consistent annotation formats, such as bounding box coordinates in COCO or YOLO, is also essential to prevent silent corruption during training. Verification scripts can help maintain format consistency across datasets.

Read the full article at Towards AI - Medium

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.