What is meant by retraining in the context of machine learning models?

Retraining in the context of machine learning models refers to the process of updating an existing model with new data to enhance its performance, adaptability, and predictive accuracy. As new data becomes available, it may contain patterns or trends that were not present during the initial training phase, making it necessary to refine the model to maintain its relevance and effectiveness. This process ensures that the model continues to provide valuable insights and predictions in a dynamic environment, where underlying data trends may change over time.

For instance, a model trained on financial data from one year may become outdated if trained data from subsequent years shows different behaviors. Retraining enables the incorporation of this more recent data, thereby potentially improving the model's accuracy and reliability. This is crucial in various applications, such as dynamic markets or evolving user preferences, where the initial model could become less effective as time progresses without regular updates.

Understanding this concept underscores the importance of not just building a model but also maintaining and updating it through retraining to ensure ongoing performance and applicability.