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A classifier's predictions are only as accurate as their training data allows. Classifiers can still make predictions with a high confidence score, even if the training data is poor. Causes of poor training data include: outliers, limited training data and poorly labelled data. It is important to test a classifier to make sure the outputs are as expected.