What are the consequences of data drift in machine learning models?

What are the consequences of data drift in machine learning models?

Data drift can lead to decreased model accuracy, as the model may not perform well on new data that differs from the training data.

How can I detect data drift in my models?

You can detect data drift using statistical tests, monitoring model performance over time, and comparing distributions of input features.

What strategies can I use to mitigate data drift?

To mitigate data drift, consider retraining your model regularly, using adaptive learning techniques, and implementing robust validation processes.

Can data drift affect all types of models?

Yes, data drift can affect any type of model, but the impact may vary depending on the model's complexity and the nature of the drift.