Model Drift: Why “Set-It and Forget-It” Doesn’t Work for Machine Learning Models — Analyzr

Data Drift

Concept Drift

Identifying And Preventing Model Drift

  • When dealing with propensity models one of the most common stats to watch for is the F1 score, an accuracy metric. F1 measures two key attributes of any model: precision and recall. Precision and recall are best explained with a visual representation, which this piece by Datatron illustrates very well.
  • For cluster-based models, often used for segmentation, the Silhouette score allows the user to measure how clearly separated and cohesive the resulting clusters are from each other. A score of -1 means clusters are poorly assigned, while a score of 1 indicates well distributed and differentiated clusters.

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team

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Analyzr: Insights, Achieved

Analyzr: Insights, Achieved

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team