📌 Ghost Parameter Retention Summary

Ghost Parameter Retention refers to the practice of keeping certain parameters or settings in a system or software, even though they are no longer in active use. These parameters may have been used by previous versions or features, but are retained to maintain compatibility or prevent errors. This approach helps ensure that updates or changes do not break existing workflows or data.

🙋🏻‍♂️ Explain Ghost Parameter Retention Simply

Imagine you have an old key on your keyring that no longer opens any doors you use, but you keep it just in case you ever need it again or to avoid confusion. Ghost Parameter Retention is like keeping that old key in a software system, so nothing gets disrupted if someone still expects it to be there.

📅 How Can it be used?

Ghost Parameter Retention can be used to avoid breaking integrations when updating an API that clients already depend on.

🗺️ Real World Examples

A company updates its web API to remove an old feature, but keeps the original parameter in the request format. Even though the parameter is ignored, this prevents client applications from failing due to unexpected changes.

A database schema includes a column that is no longer used by the current application, but is kept to ensure that legacy reports and data exports relying on its presence continue to work.

✅ FAQ

Ghost Parameter Retention is when a system keeps certain settings or options, even though they are no longer needed in daily use. This is often done to make sure that updates do not cause unexpected problems or break parts of the system that still depend on those old settings.

Keeping unused parameters helps maintain compatibility with older versions or features. It acts as a safety net, making sure that older files or workflows still work smoothly after a software update, and helps avoid errors that could appear if those settings were suddenly removed.

In most cases, keeping these unused parameters does not have a noticeable impact on performance. They usually just sit quietly in the background, making sure everything runs smoothly for users who might still rely on older features or formats.

📚 Categories

• Explainability & Interpretability
• Model Optimisation Techniques
• Model Training & Tuning

🔗 External Reference Links

Ghost Parameter Retention link

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