Out-of-Distribution (OOD) Data

Out-of-Distribution (OOD) data refers to instances that differ significantly from the training data used to build a machine learning model. These data points can lead to unpredictable model behavior, as the model may not have learned to generalize well to this new data distribution. OOD data is crucial in evaluating model robustness and reliability, particularly in real-world applications where data can vary widely. Common use cases for assessing OOD data include anomaly detection, domain adaptation, and improving model performance in dynamic environments.