Abstract: Embodiments of labeling an unlabeled dataset are provided. One embodiment comprises (a) obtaining a labeled dataset comprising a first plurality of data inputs and corresponding labels; (b) training a classification model using the labeled dataset; (c) obtaining the unlabeled dataset comprising a second plurality of data inputs without labels; (d) applying the classification model to the unlabeled dataset to generate a predicted label for each data input of the unlabeled dataset; (e) determining a verification quantity of the predicted labels to be verified by a user; (f) obtaining a verification dataset for the verification quantity of the predicted labels verified by the user; (g) updating the classification model using the verification dataset; and (h) applying the updated classification model to the remaining predicted labels that did not undergo verification and updating in response to the updated classification model. The verification dataset comprises an update to at least one predicted label.