What is "model drift" in machine learning?

What is "model drift" in machine learning?

• A. The increase in model accuracy over time
• B. The degradation of model performance due to changes in data distribution
• C. The process of enhancing a model's features
• D. The introduction of new data to a model

Answer: (B)

Model drift refers to the phenomenon where the performance of a machine learning model deteriorates over time due to changes in the underlying data distribution. This can occur because the environment in which the model operates may change, leading to shifts in the characteristics of the data the model encounters compared to the data it was trained on. When model drift occurs, the assumptions that were valid at the time of model training may no longer hold true, causing the model's predictions to become less accurate. This degradation in performance necessitates periodic retraining or updating of the model to ensure it continues to perform reliably. Understanding model drift is crucial for maintaining the effectiveness of AI systems, especially in dynamic environments where data patterns can evolve rapidly.

Model drift refers to the phenomenon where the performance of a machine learning model deteriorates over time due to changes in the underlying data distribution. This can occur because the environment in which the model operates may change, leading to shifts in the characteristics of the data the model encounters compared to the data it was trained on.

When model drift occurs, the assumptions that were valid at the time of model training may no longer hold true, causing the model's predictions to become less accurate. This degradation in performance necessitates periodic retraining or updating of the model to ensure it continues to perform reliably. Understanding model drift is crucial for maintaining the effectiveness of AI systems, especially in dynamic environments where data patterns can evolve rapidly.