1. A method for classification, the method comprising: training, by a computer system, a classification model using a training data set; after the training by the computer system of the classification model using the training data set, classifying, by the computer system, using the classification model, a first record set to generate a high confidence outcome set and a low confidence outcome set; adding, by the computer system, the high confidence outcome set to the training data set; using the low confidence outcome set as a classification outcome set, the classification outcome set including classifier-record pairings for records of the first record set, and submitting, by the computer system, the classification outcome set to a crowdsourcing computer network; receiving, by the computer system, a validated portion and a non-validated portion of the classification outcome set from the crowdsourcing computer network; adding, by the computer system, the validated portion to the training data set; transmitting, by the computer system, the non-validated portion to an analyst computer network; receiving, by the computer system, from the analyst computer network, additional training data relating to classifiers of the classifier-record pairings; adding, by the computer system, the additional training data to the training data set; retraining, by the computer system, the classification model using the training data set, including the high confidence outcome set, the validated portion, and the additional training data; and after the retraining, classifying, by the computer system, using the classification model, a second record set.