We use cosine or Euclidean distances for embedding models to make hard classifications. But this has a big limitation: no measure of confidence and no interpretability.
Instead, building a logistic regression model can turn distances into percentage based confidence scores. This also accounted for how a small decrease in distance affects the confidence score—similar to how a derivative measures sensitivity.
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[ 0.25 ms ] story [ 25.2 ms ] threadInstead, building a logistic regression model can turn distances into percentage based confidence scores. This also accounted for how a small decrease in distance affects the confidence score—similar to how a derivative measures sensitivity.