Robustness refers to the ability of a model to handle varying or disruptive conditions. Simply put, if an algorithm or model is considered robust, it means that it is able to maintain its performance accuracy despite changes in input data or noise, as well as possible disruptions or manipulation. A strong framework to identify and use models with robustness is key if we want to depend on them in real-world contexts. This is why the development of robust, machine learning models is essential; they must be reliable and enduring to ensure their effectiveness in practical use cases.
