Which of the following best describes model drift?

Model drift refers to the gradual decline in the performance of a predictive model over time, which can occur due to changes in the underlying data patterns or relationships that the model was trained on. This phenomenon happens when the statistical characteristics of the input data change, leading the model to become less effective at making predictions. By describing model drift as a change in model performance over time, this answer captures the essence of the concept and the potential need for model retraining or refinement to maintain accuracy.

The other options do not accurately represent the concept of model drift. A sudden drop in training accuracy could indicate various issues, such as problems with the data or the model training process itself, but it doesn't inherently reflect the gradual changes associated with model drift. Improvement of model speed relates to computational efficiency rather than predictive accuracy or data changes. Increased user engagement is not directly related to the model's performance over time and addresses user behavior, which is outside the scope of model drift.