What are some effective techniques to handle model drift in machine learning?

What are some effective techniques to handle model drift in machine learning?

There are several techniques to handle model drift, including retraining models, using ensemble methods, and monitoring performance.

Can you explain how retraining models works?

Retraining models involves updating the model with new data to ensure it remains accurate and relevant as data patterns change.

What is the role of ensemble methods in addressing model drift?

Ensemble methods combine multiple models to improve robustness and accuracy, helping to mitigate the effects of model drift.

How can I monitor model performance effectively?

You can monitor model performance using metrics like accuracy, precision, recall, and by setting up alerts for significant drops in performance.