robustness in machine learning

Robustness in machine learning refers to a model's ability to maintain accurate performance when faced with new, unseen data or slight variations in input. It means the model can handle noise, errors, or unexpected differences without significantly degrading its results. Essentially, a robust system is reliable and stable across diverse real-world situations, ensuring that slight changes or imperfections in data do not cause major mistakes. This quality is important for creating trustworthy AI that works well outside controlled environments.