📌 Model Lag Summary
Model lag refers to the delay between when a machine learning model is trained and when it is actually used to make predictions. This gap means the model might not reflect the latest data or trends, which can reduce its accuracy. Model lag is especially important in fast-changing environments where new information quickly becomes available.
🙋🏻♂️ Explain Model Lag Simply
Imagine you study for a test using last year’s textbook, but the teacher has updated the material since then. Your answers might be out of date because you did not have the newest information. Model lag is like using an old textbook when the world has already moved on.
📅 How Can it be used?
Monitor and minimise model lag to keep prediction results relevant and accurate in dynamic data environments.
🗺️ Real World Examples
A retail company uses a model to predict product demand, but if the model is not updated with recent sales data, it may miss new shopping trends, leading to overstock or shortages.
A bank uses a fraud detection model trained on last year’s transaction data, but as scammers change tactics, the model’s effectiveness drops if it is not retrained regularly, allowing more fraudulent transactions to slip through.
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