1. A method, comprising: selecting a training dataset comprising training instances having respective training features; applying a classifier to the training dataset, thereby generating a training classification that assigns, to each of the training instances, one of a plurality of categories, the classifier having an expected classification; detecting a classification bias in the training classification relative to the expected classification; defining, in response to the classification bias, a calibration matrix based on conditional probabilities of features of a production dataset given occurrences of corresponding training features; selecting a production dataset comprising production instances; and applying the classifier and the calibration matrix to the production dataset, thereby generating a production classification quantification that assigns, to each of the production instances, one of the plurality of categories.