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Label coverage

As explained in Delivering Machine Learning Projects: From design to deployment

Label coverage is the completeness of labels with respect to the items and relevant objects represented in a data set.

Label coverage is an EDA checkpoint for testing whether labels account for the items and relevant objects represented across a data set. section 6.1.4

Turn coverage into a review checklist:

  • Check for completely unlabeled images. section 6.1.4
  • Check whether objects appearing in several images remain unlabeled. section 6.1.4
  • Compare the label inventory with the objects actually represented, including surprisingly absent categories. section 6.1.4

Household-tool inventory

For images expected to contain household tools, verify that labels cover screwdrivers, hammers, and wrenches, while checking for missing saws or pliers. section 6.1.4

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Checks unlabeled images, repeated unlabeled objects, and missing categories, but does not define a numerical coverage formula.
Examples
Uses household tools, repeated objects, unlabeled images, and missing saws or pliers as inspection targets.
What it assumes
Assumes an image data set with an associated label set and teaches completeness checks through direct inspection.
What you can do after
Diagnose missing or incomplete labels before using the data to solve the business problem.
How it connects
Links label completeness with capture bias, environment, image positioning, and exploratory data analysis.
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Related concepts

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