Under what condition should you retrain a model with updated datasets?

Under what condition should you retrain a model with updated datasets?

• A. Never; retraining is never needed
• B. If the dataset size remains the same
• C. If the model is small and feature weights are updated
• D. If the model is large and heavy

Answer: (C)

Retraining with updated data is most appropriate when you can actually incorporate the new information into the model without excessive cost or downtime. In practice, a smaller model is ideal because you can quickly adjust and re-estimate its parameters, especially the feature weights, so the model quickly reflects new patterns in the data. If the model is large and heavy, retraining becomes resource-intensive and time-consuming, making it less practical unless the update is crucial. The other statements aren’t reliable guides on their own: dataset size staying the same doesn’t imply retraining isn’t needed if data distributions have shifted, and never retraining ignores the need to adapt to new information.

Retraining with updated data is most appropriate when you can actually incorporate the new information into the model without excessive cost or downtime. In practice, a smaller model is ideal because you can quickly adjust and re-estimate its parameters, especially the feature weights, so the model quickly reflects new patterns in the data.

If the model is large and heavy, retraining becomes resource-intensive and time-consuming, making it less practical unless the update is crucial. The other statements aren’t reliable guides on their own: dataset size staying the same doesn’t imply retraining isn’t needed if data distributions have shifted, and never retraining ignores the need to adapt to new information.