![]() For instance, when a traffic sign detector does not operate well in certain daylight conditions or for unexpected inputs, even though the overall accuracy of the model may be high, it is still important for the development team to know ahead of time about the fact that the model may not be as reliable in such situations.įigure 1 - Error Analysis moves away from aggregate accuracy metrics, exposes the distribution of errors to developers in a transparent way, and enables them to identify & diagnose errors efficiently. Often, such failures may cause direct consequences related to lack of reliability and safety, unfairness, or more broadly lack of trust in machine learning altogether. How often do we read claims such as “Model X is 90% on a given benchmark.” and wonder what does this claim mean for practical usage of the model? In practice, teams are well aware that model accuracy may not be uniform across subgroups of data and that there might exist input conditions for which the model fails more often. Machine Learning (ML) teams who deploy models in the real world often face the challenges of conducting rigorous performance evaluation and testing for ML models.
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