robuustheidsmetrieken

Robustness metrics are quantitative measures used to assess the resilience and reliability of systems, models, or processes when faced with uncertainty, noise, or variations in input data. These metrics help understand how well a system maintains its performance or integrity under adverse conditions. Commonly, robustness is evaluated by introducing specific types of perturbations, such as random noise, adversarial attacks, or missing data, and then observing the system's response.

One key aspect of robustness metrics is their ability to quantify degradation. For example, in machine learning, a robustness metric might measure the drop in accuracy when the input data is corrupted by a certain level of noise. Similarly, in software engineering, robustness metrics could track the number of crashes or errors encountered when the system is subjected to unexpected inputs or high load. The choice of metric depends heavily on the domain and the specific failure modes being considered.

Statistical measures are often employed. This can include calculating the standard deviation of performance across different simulated scenarios or determining the probability of failure within a given range of input variations. Metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) can also serve as robustness indicators when applied to the difference between ideal and perturbed outputs. Ultimately, robustness metrics provide a standardized way to compare different systems or configurations and to set performance targets for reliability.