What is the term for a machine learning model's performance decline due to changes in its environment?

The term that describes a machine learning model's performance decline due to changes in its environment is known as Model Drift. This phenomenon occurs when the statistical properties of the target variable, or the relationships between input features and the output, change over time. As a result, a model that once performed well on the training data may no longer make accurate predictions when applied to new, real-world data, leading to a degradation in its performance.

Model Drift can arise from various factors, including changes in consumer behavior, market conditions, or data distribution. It's essential for practitioners to monitor the performance of their models continuously and implement strategies to detect and address model drift, such as retraining the model with updated data or using adaptive algorithms that can better handle evolving environments.

Understanding Model Drift is crucial for maintaining the effectiveness of machine learning applications in dynamic settings, ensuring that models remain relevant and accurate over time.