Multi-label classification utilizes data where each example can belong to multiple (or none) of the K classes. One example of this could be an image of a face that is labeled with wearing_glasses and wearing_necklace as opposed to standard multi-class classification where each example has only one label.
Ensuring high quality labels in multi-label classification datasets is really hard, as they often contain tons of tagging errors because annotating such data requires many decisions per example.
This article explores the challenges of multi-label data quality and demonstrate how to automatically identify and rectify problems with an enterprise no-code AI data correction tool.
Ensuring high quality labels in multi-label classification datasets is really hard, as they often contain tons of tagging errors because annotating such data requires many decisions per example.
This article explores the challenges of multi-label data quality and demonstrate how to automatically identify and rectify problems with an enterprise no-code AI data correction tool.