What is data drift?
Data drift refers to the change in the statistical properties of a dataset over time, which can affect model performance.
How does data drift impact machine learning models?
It can lead to decreased accuracy and reliability of predictions, as the model may not perform well on new data.
What are some common causes of data drift?
Common causes include changes in user behavior, external factors like market trends, or updates in data collection methods.
How can I detect data drift?
You can use statistical tests, monitor model performance metrics, or implement drift detection algorithms.
What can I do to mitigate data drift?
Regularly retrain your model with new data, use adaptive learning techniques, and monitor data quality continuously.
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