griko

To help with the SFRame gymnastics, after running: `e = cls.evaluate(test)` You could do: 1) `e['confusion_matrix'].sort('count', ascending=False)` 2) `e['confusion_matrix'][e['confusion_matrix'].apply(lambda r: r['target_label'] != r['predicted_label'])].sort('count', ascending=False)` 1 - lists pairs of target-predicted...