A Characterization of Prediction Errors
This work addresses the fundamental issue of error analysis in predictive systems, which is critical for building effective models, but it appears incremental as it builds on existing error characterization concepts.
The paper tackles the problem of understanding and fixing prediction errors in predictive systems by delineating four types that characterize all such errors, and it describes potential remedies and tools to reduce uncertainty in identifying and addressing these errors.
Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to reduce the uncertainty when trying to determine the source of a prediction error and when trying to take action to remove a prediction errors.