What does “model drift” refer to?

Model drift refers to the phenomenon where a model's performance degrades over time due to changes in the underlying data distribution on which it was trained. This occurs when the statistical properties of the target variable, or the relationships between input features, change in such a way that the model's predictions become less accurate. Essentially, if a model was trained on historical data, as new data points are collected, those data points may not reflect the same patterns that the model learned, leading to decreased performance.

Understanding model drift is critical for maintaining the effectiveness of AI solutions, as it indicates the need for regular monitoring and potential recalibration or retraining of the model to ensure it remains aligned with current data trends. In practice, organizations may use techniques such as periodic retraining on new data or implementing continuous monitoring systems to detect drift and adapt accordingly.

The other choices do not accurately capture the concept of model drift. Instant model switching does not relate to performance degradation, a sudden increase in efficiency does not signify drift, and techniques for improving models refer to enhancements rather than the concept of model drift itself.