What does the term "data drift" refer to in machine learning?

The term "data drift" refers specifically to changes in the data distribution over time that can impact model performance. It highlights the phenomenon where the statistical properties of the data that a machine learning model was trained on evolve, potentially leading to a decrease in the model's accuracy or reliability when it encounters new data. This change can occur due to various factors, such as seasonal trends, shifts in user behavior, or changes in the data collection process itself.

In contrast, the other options do not accurately define data drift. The increase in training data size over time pertains to dataset expansion but does not directly relate to changes in the underlying data distribution. Variance between training and testing datasets may indicate model performance issues but is not synonymous with data drift, which focuses specifically on the temporal changes in data characteristics. Lastly, while adjusting existing models to newer data is a response to data drift, it does not define what data drift itself is. Understanding data drift is crucial for maintaining effective machine learning models, as it underscores the importance of monitoring and potentially retraining models based on evolving data trends.