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How to Spot and Prevent Model Drift Before it Impacts Your Business
- Model drift can impact the stability and reliability of models in production, leading to decreased accuracy over time.
- Score drift and feature drift are common types of drift that affect model performance by altering score distributions and feature relationships.
- Monitoring model scores using techniques such as Population Stability Index (PSI) can detect significant drift between datasets.
- The Kolmogorov-Smirnov (K-S) test is effective for detecting drift in numeric features without assuming a normal distribution.
- Chi-Square test is useful for identifying shifts in categorical and boolean features by comparing frequency distributions.
- Spearman correlation helps track shifts in relationships between features, detecting changes even in non-linear trends.
- Autoencoders are valuable for detecting high-dimensional multivariate shifts by capturing complex, non-linear dependencies across variables.
- Model monitoring is crucial for data scientists and ML engineers to detect trends like an increase in potential fraud due to shifts in data.
- Advanced monitoring techniques help identify underlying issues that may impact model performance and require further investigation.
- Understanding statistical techniques for drift detection is important to complement automated tools like evidently.ai.
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