Are we preventing Concept and Data Drift?
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Are we preventing Concept and Data Drift?
- Data Drift weakens performance because the model receives data on which it hasn’t been trained.
- With Concept Drift the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways causing accuracy issues.
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Select an appropriate drift detection algorithm and apply it separately to labels, model’s predictions and data features.
- Incorporate monitoring mechanisms to detect potential errors early.
Interesting resources/references
- Data Drift vs. Concept Drift
- Characterizing Concept Drift
- Inferring Concept Drift Without Labeled Data
- Automatic Learning to Detect Concept Drift
- From concept drift to model degradation: An overview on performance-aware drift detectors
- Learning under Concept Drift: A Review
- Detect data drift (preview) on datasets