Model robustness refers to the ability of a machine learning model to maintain its performance and accuracy even when exposed to noisy, incomplete, or diverse data during training and testing. This concept is closely related to regularization techniques that help prevent overfitting, ensuring that the model generalizes well to unseen data. By incorporating methods such as dropout and other noise-based regularization techniques, models become more resilient against variations in input data, leading to improved reliability in real-world applications.
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