pairwise label equivalence

Pairwise label equivalence refers to a situation where two or more labels in a classification task are considered equivalent or interchangeable in pairs. This means that if two labels are pairwise equivalent, then they cannot be distinguished from each other based on the available data, and any model trained on the data should treat them as the same.

For example, in a sentiment analysis task, the labels "positive" and "very positive" may be considered pairwise equivalent, as they both express a generally positive sentiment. Similarly, the labels "neutral" and "unknown" may be considered pairwise equivalent in some contexts, as they both indicate a lack of information or opinion.

Pairwise label equivalence is important to consider when designing and evaluating machine learning models, as it can affect the accuracy and fairness of the model. If pairwise equivalent labels are not treated as equivalent, the model may assign different probabilities or predictions to them, leading to biased or inaccurate results.