📌 Model Retraining Metrics Summary
Model retraining metrics are measurements used to evaluate how well a machine learning model performs after it has been updated with new data. These metrics help decide if the retrained model is better, worse, or unchanged compared to the previous version. Common metrics include accuracy, precision, recall, and loss, depending on the specific task.
🙋🏻♂️ Explain Model Retraining Metrics Simply
Imagine you are updating a recipe to make your biscuits taste better. After each change, you taste the new batch and rate it to see if it improved. Model retraining metrics are like those taste tests, helping you decide if the model has actually got better after retraining.
📅 How Can it be used?
Model retraining metrics help teams decide when to update a predictive model and measure if the new version is an improvement.
🗺️ Real World Examples
An online retailer uses a recommendation system to suggest products to customers. As shopping trends change, the company retrains its model with recent purchase data. It uses retraining metrics like click-through rate and conversion rate to ensure the updated model offers better suggestions than before.
A hospital updates its disease prediction model with new patient records every month. By tracking retraining metrics such as accuracy and false positive rates, the hospital ensures the updated model provides safer and more reliable diagnoses.
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