Characteristic Stability Index (CSI) is a measure of the stability of the distribution of the corresponding feature. In the figure above, the blue line shows CSI, and the orange line shows the Covariate Shift (CS) . The plots show the results of monitoring the stability of the distributions of the independent variables over time. The red line represents the warning threshold, which is a value of 0.25. If the CSI or CS exceeds the warning threshold, it indicates that there may be a problem with the stability of the corresponding feature, and further investigation may be necessary.
Conclusion
In today's dynamic data-driven world, maintaining the accuracy, reliability and value of machine learning models is crucial. Businesses need a robust monitoring framework to navigate the complexities and challenges of model drift, feature importance, and bias detection. Businesses can ensure accurate model predictions, address drift, monitor feature importance, and ensure fairness through a robust monitoring framework. By leveraging advanced techniques and continuous monitoring, enterprises can uphold the integrity and performance of their ML models, driving better decision-making and improved outcomes.
Author
Olena Fylymonova Consultant, Strategic Center
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