Model robustness refers to the ability of a machine learning model to maintain its performance despite variations in input data, noise, or changes in the environment. A robust model is designed to generalize well, ensuring that it performs consistently across different datasets, including those it has not encountered during training. This characteristic is essential for reliable predictions in real-world applications, where data can be unpredictable.
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