What does the process of retraining a model generally aim to achieve?

The primary aim of retraining a model is to enhance model performance. This is achieved by updating the model with new data or refining the existing model based on insights gained from previous performance evaluations. As you retrain a model, you can incorporate additional data, which may capture new trends or patterns that have emerged since the initial training. This helps the model make more accurate predictions or classifications, adapting to any changes in the underlying data distribution or the problem it is designed to solve.

Retraining is particularly important in dynamic environments where data is continuously changing. By periodically retraining the model, data scientists can ensure that the model remains relevant and useful for decision-making processes. The improvements in model performance can manifest in various metrics, such as accuracy, recall, precision, or F1 score, depending on the specific use case.

Though other options may seem relevant to model development in different contexts, they do not capture the core intent behind retraining, which is fundamentally about improving how well the model performs on its tasks.