1. A computer-implemented method comprising: receiving a plurality of training examples; training a plurality of different types of predictive models using the received training examples, wherein each of the predictive models implements a different machine learning technique; measuring the performance of each trained model; computing a suggestion score for each training example according to each respective trained model, wherein the suggestion score is based on one or more factors that indicate whether additional examples similar to the training example would improve the performance of the model, including weighting each suggestion score by the measured performance of the respective trained model; combining the computed suggestion scores for each training example to compute an overall suggestion score for each training example; and ranking the training examples by overall suggestion scores.
7. The method of claim 1 , wherein one of the one or more factors is a sparseness score, wherein the sparseness score for a particular training example is based on comparing a distribution of training example answers to the answer of the particular training example.