  Industry datasets used for text classification are rarely created for that
purpose. In most cases, the data and target predictions are a by-product of
accumulated historical data, typically fraught with noise, present in both the
text-based document, as well as in the targeted labels. In this work, we
address the question of how well performance metrics computed on noisy,
historical data reflect the performance on the intended future machine learning
model input. The results demonstrate the utility of dirty training datasets
used to build prediction models for cleaner (and different) prediction inputs.
