Model Retraining
Model retraining is the process of updating a machine learning model with new data to improve its accuracy and performance. As new data becomes available, the model is retrained to incorporate the new information and adjust its parameters. This is particularly important in dynamic environments where the underlying data distribution may change over time. Retraining can be done periodically or triggered by specific events, such as a significant change in the input data or a drop in model performance. The retraining process involves selecting the appropriate algorithm, preparing the data, and fine-tuning the model parameters to optimize its performance. Model retraining is a critical component of machine learning systems that require continuous learning and adaptation to remain effective.
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