label shift
What It Means
Label shift occurs when the proportion of different outcomes in your data changes over time, but the underlying relationship between inputs and outcomes stays the same. For example, if your fraud detection system was trained when 2% of transactions were fraudulent, but now 5% are fraudulent, you're experiencing label shift. The characteristics that make a transaction fraudulent haven't changed, just how common fraud has become.
Why Chief AI Officers Care
Label shift can cause significant drops in model performance and business metrics without any obvious warning signs, since traditional monitoring might miss this type of change. It requires specific recalibration strategies rather than full model retraining, which can save significant time and resources. If not addressed, it leads to systematic over or under-prediction of important business outcomes like customer churn, fraud, or demand forecasting.
Real-World Example
A credit card company's fraud detection model was trained during normal economic times when fraud represented 1.5% of transactions. During an economic downturn, fraud rates jumped to 4% of transactions, but the patterns that indicate fraud (unusual spending locations, transaction amounts, timing) remained identical. The model's alerts became unreliable because it wasn't calibrated for the new baseline fraud rate, even though it could still recognize fraudulent behavior patterns perfectly.
Common Confusion
People often confuse label shift with concept drift, but they're different problems requiring different solutions. Label shift means the underlying patterns haven't changed, just their frequency, while concept drift means the actual relationship between inputs and outputs has fundamentally changed.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, label shift occurs when disease prevalence changes over time while the diagnostic relationship between sy...
Finance: In finance, label shift commonly occurs in credit risk models when economic conditions change the default rate distribut...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. ... We work with the label shift assumption, i.e., ps(x|y) = pt(x|y)"Source: saurabh_label_2020
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