Why might model performance in production differ from its performance during training and evaluation?
Why might model performance in production differ from its performance during training and evaluation?
Real-world conditions and data may vary from the training environment.
Explanation:
A machine learning model may perform differently in production because the real-world data, user behavior, or conditions can change compared to the data used during training and evaluation. This is known as data drift or distribution shift.
Why not the others?
- a) Production data is never different from training data → Incorrect; production data often changes over time.
- c) The model always performs better in production → Incorrect; performance can improve or decline.
- d) Model performance remains constant → Incorrect; model accuracy can change as conditions evolve.
