Which method should the Specialist try to improve model performance?
The model needs to be completely re-engineered because it is unable to handle product inventory changes.
The model’s hyperparameters should be periodically updated to prevent drift.
The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
The model should be periodically retrained using the original training data plus new data as product inventory changes.
Explanations:
Completely re-engineering the model may not be necessary. While the model may struggle with inventory changes, it can often be improved through retraining rather than a full redesign.
While hyperparameter tuning can help, it is typically not sufficient on its own to address performance issues stemming from data drift or changing user behavior. Regular updates are essential, but retraining is more critical for maintaining performance over time.
Periodically retraining from scratch using the original data does not account for the changes in user behavior or product inventory over time. Additionally, adding a regularization term may not directly address the problem of diminishing returns from the model.
Periodically retraining the model using both the original training data and new data allows it to adapt to changes in customer behavior and product inventory. This approach helps the model remain relevant and effective in providing recommendations.
If I’m correct, the answer is:
The model should be periodically retrained using the original training data plus new data as product inventory changes.
To the best of my knowledge, the answer is:
The model should be periodically retrained using the original training data plus new data as product inventory changes.