👉 Coverage engineering is a process used in the deployment and optimization of machine learning models, particularly in scenarios where the model must make predictions on a large volume of data that may not be uniformly distributed across different regions or features. It involves designing and implementing strategies to ensure that the model's performance remains consistent across all parts of the input space, even when data is sparse or unevenly distributed. This can include techniques such as stratified sampling to balance the representation of different data points, data augmentation to artificially increase the diversity of the training set, and model architecture adjustments to better capture patterns in underrepresented areas. The goal is to prevent the model from becoming biased towards the majority classes or regions, thereby improving overall robustness and generalization capabilities.
