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

Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data