What Data Drift Meaning, Applications & Example
Changes in data distribution over time that affect model performance.
What is Data Drift?
Data Drift refers to the change in the statistical properties of the input data over time, which can lead to a decline in model performance. It occurs when the data distribution shifts from the one that was used to train the model, making predictions less accurate.
Causes of Data Drift
- Changes in External Factors: Economic shifts, user behavior changes, or environmental factors can affect the data.
- Model Inaccuracy: A model might rely on outdated data patterns, leading to incorrect predictions when the data shifts.
- Data Collection Changes: Adjustments in how data is collected or processed can cause differences from training data.
Applications of Data Drift
- Model Monitoring: Continuously tracking model performance and data characteristics to detect data drift and retrain models as needed.
- Fraud Detection: In financial systems, data drift can affect fraud detection algorithms, requiring adjustments to handle new patterns of fraudulent behavior.
- Recommendation Systems: In e-commerce or content platforms, data drift can change user preferences, requiring models to adapt for better recommendations.
Example of Data Drift
In e-commerce, a recommendation system that was trained on historical purchasing data may start underperforming when user preferences change over time, such as during a seasonal shift or the introduction of new product categories. Detecting and addressing data drift can ensure the system provides more accurate suggestions.