1. A computer-implemented method comprising: calculating one or more types of suggestion scores for each of a plurality of training examples, wherein each type of suggestion score is based at least in part on a plurality of computed predictions for each training example by generated by a plurality of different trained models, including weighting each type of suggestion score by an accuracy of the trained model that generated the prediction; calculating an overall suggestion score for each training example based at least in part on a combination of the one or more types of suggestion scores for each training example; ranking the training examples by the corresponding overall suggestion scores; and providing one or more highest-ranked training examples as a set of suggested training examples.
1. A computer-implemented method comprising: calculating one or more types of suggestion scores for each of a plurality of training examples, wherein each type of suggestion score is based at least in part on a plurality of computed predictions for each training example by generated by a plurality of different trained models, including weighting each type of suggestion score by an accuracy of the trained model that generated the prediction; calculating an overall suggestion score for each training example based at least in part on a combination of the one or more types of suggestion scores for each training example; ranking the training examples by the corresponding overall suggestion scores; and providing one or more highest-ranked training examples as a set of suggested training examples. 4. The method of claim 1 , wherein one of the one or more types of suggestion scores is a difficulty score, wherein the difficulty score for a particular training example in the training examples is based on comparing a confidence associated with an incorrectly predicted category for the training example to a threshold.