Domain Shift

A Domain Shift is a distribution change that represents variation between training and deployment environments.

• AKA: Distribution Shift, Dataset Shift, Training-Deployment Mismatch, Environmental Change, Domain Change.
• Context:
  • It can typically degrade model performance in production systems.
  • It can typically require adaptation mechanisms for robustness maintenance.
  • It can often correlate with generalization distance in ML systems.
  • It can often trigger retraining requirements for model updates.
  • It can range from being a Minor Domain Shift to being a Major Domain Shift, depending on its magnitude level.
  • It can range from being a Gradual Domain Shift to being a Sudden Domain Shift, depending on its temporal pattern.
  • It can range from being a Covariate Domain Shift to being a Concept Domain Shift, depending on its shift type.
  • It can range from being a Detectable Domain Shift to being a Hidden Domain Shift, depending on its observability level.
  • ...
• Example(s):
  • Temporal Domain Shift, such as:
    • Seasonal Domain Shift for weather prediction models.
    • Economic Cycle Domain Shift for financial models.
  • Geographic Domain Shift, such as:
    • Cross-Country Domain Shift for recommendation systems.
    • Urban-Rural Domain Shift for healthcare models.
  • ...
• Counter-Example(s):
  • IID Data Distribution, which maintains consistency.
  • Stationary Process, which lacks distribution change.
  • Controlled Environment, which prevents shift occurrence.
• See: Machine Learning Concept, Generalization Distance, Distance from the Known, Covariate Shift, Concept Drift, Transfer Learning, Domain Adaptation, Robustness Measure, ML Model Deployment.

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