Introduction to model drift
ML models can experience a decline in performance over time, which is a common issue in projects. The main reasons for this are changes in the input data that is fed into the model. These changes can occur due to various reasons, such as the underlying distribution of the data changing, an alteration in the relationship between the dependent and independent features, or changes in the source system that generates the data itself.
The performance degradation of deployed models over time is called Model Drift. To effectively identify instances of Model Drift, various metrics can be monitored:
- Accuracy: A declining trend in accuracy can serve as a strong indicator of model drift.
- Precision and Recall: A noticeable decrease in these values may highlight the model's diminishing ability to make accurate and relevant predictions.
- F1 Score: This is a harmonized metric that encapsulates both precision and recall. A drop in the F1 Score...
