Model retraining is the process of training an existing machine learning model again using new, updated, or improved data. It helps the model adapt when data patterns, user behavior, business requirements, or real-world conditions change. During retraining, updated data is prepared and used to adjust the model’s learned parameters, followed by testing and validation before a new version is deployed.
For example, a fraud detection model may be retrained with recent transaction data as fraud techniques evolve. This helps maintain prediction accuracy and reduce model drift. Model retraining is useful for recommendation systems, forecasting, fraud detection, customer analytics, and other applications where changing data can affect model performance.
