An unsupervised method borrowed from computer vision that sets optimal per-label confidence cut-offs in query classification by maximizing the variance between positive and negative score clusters.
Otsu's Thresholding (for Queries) is a dynamic, unsupervised method borrowed from computer vision and applied to search query classification. Rather than fixing one confidence cut-off by hand, it sets an optimal per-label threshold automatically by maximizing the variance between the positive and negative score clusters, in effect finding the split point that separates "this label applies" from "it does not" as cleanly as the data allows.
Because the cut-off adapts per label, it handles the reality that different query classes carry different score distributions. It is directly relevant to Query Classification and to decision points like Query Deserves Grounding, and it is the kind of calibration a system such as the Grounding Classifier relies on to stay accurate.
