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Turn model output into a defined, reviewable decision.
Classification work begins with a label vocabulary and the meaning of each possible outcome. These articles cover evaluation, uncertain cases and the relationship between evidence and a returned label. They are useful for routing, document labeling and other bounded decisions where fluent prose is not the final product.
Read the classification guide for the decision workflow, and the provenance article when labels depend on external records. For every class, collect examples that belong, examples that almost belong and cases that should be reviewed. A useful system needs a policy for the gaps between classes as well as the easy matches.
2 articles tagged Classification
Build traceable AI data workflows for tokenized assets by preserving source identity, observation times and derivation records. Separate claims from conclusions and evaluate classification with realistic evidence gaps. Connect each model result to the records that supported it.
7 min readTurn classifier outputs into decisions that can be evaluated. Define label semantics, distinguish scores from calibrated probabilities, choose review thresholds, and measure precision, recall, coverage, and failures across realistic inputs.
7 min read
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