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Use the Drift and Stability of Data to Build More Resilient Models
Predictive models are only as good as the data that powers themImage by borchee, licensed from iStock
When building predictive models, model accuracy, measured by metrics like precision, recall and area under the curve (AUC), has traditionally been the primary driver of model design and operationalization. While this leads to high-fidelity model construction at training and testing time, performance in production often degrades, producing results that are worse than expected.
As machine learning (ML) matures within organizations, resiliency …