Robustness
Robustness refers to the ability of a model or system to perform well and maintain its performance under different conditions, such as changes in the input data, presence of outliers, or variations in the environment. In data science and artificial intelligence, robustness is a desirable property that ensures the reliability and generalizability of the models, as well as their resistance to errors and adversarial attacks. Robustness can be achieved through various techniques, such as data augmentation, regularization, ensemble methods, and adversarial training. Robustness is particularly important in safety-critical applications, such as autonomous driving, medical diagnosis, and financial forecasting, where errors or failures can have severe consequences.
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