TECHNIQUES FOR EVALUATION, BUILDING AND/OR RETRAINING OF A CLASSIFICATION MODEL

Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.