1. A method for representing accuracy of a trained classifier in predicting classifications for a test set of data items wherein each data item of the test set has a designated actual class of a plurality of classes, the method comprising: receiving the test set of data items and the designated actual class for each data item; receiving, for a particular data item of the test set, rankings for a set of predicted classes of the plurality of classes, wherein the set of predicted classes is ordered within the rankings based on a prediction score assigned to each individual predicted class of the set of predicted classes by the trained classifier, the prediction score indicating a confidence of the trained classifier that the individual predicted class is the designated actual class for the particular data item; comparing the designated actual class for the particular data item to each class of a top K predicted classes of the set of predicted classes as indicated by the rankings for the particular data item, wherein K is greater than one; generating a confusion matrix; in response to a determination that the designated actual class for the particular data item matches any of the top K predicted classes, then designating the trained classifier as accurate with respect to at least the particular data item by at least incrementing a particular cell of the confusion matrix identified by an actual class which is the designated actual class for the particular data item and a predicted class which is the designated actual class for the particular data item; receiving user input directed to the particular cell of the confusion matrix; in response to the user input, generating a listing of one or more data items corresponding to the particular cell of the confusion matrix; and wherein the method is performed by one or more computing devices.