Robustness Testing

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Robustness testing checks whether a machine learning model can handle situations that differ from the predictable data it saw during training. In the real world, inputs may be unusual or even intentionally manipulated. The goal of robustness testing is to see how well the model holds up under these conditions and to identify where it might fail before it’s deployed.

To do this, teams create stress tests that expose the model to tougher scenarios. They might add noise to inputs, simulate new environments, change data distributions, or create adversarial examples to see how the model reacts. The results are then compared to normal test performance to spot drops in accuracy or surprising behaviors. Insights from these tests often lead to adjustments in the model, improvements in the data, or updates to monitoring systems.

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